AUTOMATED SKIN CANCER DETECTION SYSTEM AND METHOD THEREOF

Abstract

Disclosed herein is an automated skin cancer detection system and method thereof (100) that comprises a dermatoscopic camera (102) that captures high-resolution images of skin lesions. The preprocessing unit (104) standardizes and augments these images through techniques like contrast enhancement and scaling. A feature extraction module (106) identifies key characteristics such as edges, textures, and color variations. The deep learning computational unit (108) uses advanced pre-trained models with attention mechanisms and residual connections to achieve a high accuracy of 96.34 percent. The classification module (110) employs transfer learning and hyperparameter tuning to categorize images into melanoma and non-melanoma classes. The evaluation and validation module (112) assesses model performance using metrics like accuracy, ROC-AUC, and PR-AUC. The system stores and manages data in a secure database (114) and provides real-time feedback via the visualization and feedback interface (116).

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of medical diagnostics, more specifically, relates to automated skin cancer detection system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] The " automated skin cancer detection system and method thereof " presents significant advantages in the field of dermatology and medical diagnostics, addressing key challenges associated with melanoma detection through its innovative approach and advanced algorithms. One major advantage lies in its high accuracy and reliability in identifying melanoma at an early stage. By leveraging a comprehensive dataset of over 33,000 dermatoscopic images, this system continuously analyses image patterns using deep learning models optimized for precision, with VGG19 showing an impressive accuracy of 95.64%. This high precision enables healthcare providers to detect malignant lesions earlier and with greater confidence, ultimately improving patient outcomes by initiating treatment sooner.
[0003] Another advantage of this system involves its capacity to differentiate between malignant and benign skin lesions using specialized convolutional neural networks (CNNs) and transfer learning techniques. With models like ResNet50, AlexNet, and MobileNet trained on extensive, generalized datasets, the system employs transfer learning to enhance these models specifically for melanoma detection. This transfer learning approach enhances its performance without the need for retraining from scratch, enabling the detection model to focus effectively on unique melanoma indicators in dermatoscopic images, which results in more accurate predictions. Consequently, the system supports dermatologists by minimizing false positives and negatives, making diagnosis more dependable and reducing the need for unnecessary biopsies.
[0004] Additionally, the automated skin cancer detection system demonstrates versatility and adaptability by integrating with various diagnostic tools and existing healthcare technologies. The system's convolutional neural network (CNN)-based structure adapts to diverse input types and diagnostic equipment, enhancing its usability in a range of clinical environments. This versatility contributes to cost-efficiency, as it enables clinics and hospitals to incorporate the detection system without extensive equipment upgrades. Furthermore, its adaptability to different image analysis models facilitates continuous advancements, allowing the system to stay updated with the latest in diagnostic technology. This ensures that healthcare facilities utilizing this invention can maintain cutting-edge standards in melanoma detection, resulting in an accessible, scalable, and future-proof diagnostic tool.
[0005] Existing melanoma detection inventions face several significant disadvantages that limit their effectiveness and accessibility. One primary drawback involves their reliance on general-purpose algorithms, which often lack the specificity needed for dermatological applications. Many systems use standard convolutional neural network (CNN) architectures without tailoring them for melanoma-specific features. This approach reduces accuracy in distinguishing subtle differences between malignant and benign lesions, leading to a higher rate of misdiagnosis or unnecessary biopsies. Without specialized model adjustments, these systems struggle to capture the unique textures and patterns associated with melanoma, resulting in inconsistencies in diagnosis across diverse patient profiles.
[0006] Another disadvantage lies in the limited adaptability of these systems to varying image qualities and clinical settings. Many existing inventions require high-resolution, standardized dermatoscopic images to function effectively, making them less versatile in real-world scenarios where lighting, angle, and skin tone can vary greatly. Consequently, these models often struggle outside controlled clinical environments, which restricts their usability in general practices, rural clinics, or remote diagnostic settings. This limitation not only reduces accessibility but also restricts the scope of these technologies in regions with limited resources, where early melanoma detection remains critical.
[0007] A third disadvantage stems from the absence of efficient integration with broader healthcare workflows and technologies, often leading to increased costs and operational challenges. Many existing systems require extensive retraining or custom hardware to operate optimally, which complicates implementation in healthcare facilities already using other diagnostic tools. These operational challenges drive up costs and deter widespread adoption, especially in resource-limited settings. Additionally, due to their lack of interoperability, these systems struggle to keep pace with advancements in other diagnostic technologies, limiting their potential for long-term applicability and making it challenging for clinics to continuously update them to meet evolving diagnostic standards.
[0008] Thus, in light of the above-stated discussion, there exists a need for an automated skin cancer detection system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an automated skin cancer detection system and method thereof which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to enhance early detection accuracy of melanoma through the integration of advanced image classification techniques, reducing misdiagnosis and unnecessary medical interventions.
[0012] Another objective of the present disclosure is to develop a model specifically optimized for detecting melanoma features in dermatoscopic images, ensuring greater sensitivity to malignant patterns and textures.
[0013] Another objective of the present disclosure is to leverage transfer learning to adapt pre-trained models like VGG19 and ResNet50 for skin lesion analysis, increasing efficiency and precision in melanoma detection.
[0014] Another objective of the present disclosure is to create an adaptable solution that performs consistently across varying image qualities and settings, making it suitable for use in clinical, rural, and remote diagnostic environments.
[0015] Another objective of the present disclosure is to streamline the diagnostic process by providing a fast and accurate image classification system, thus supporting clinicians in making timely and informed decisions regarding melanoma treatment.
[0016] Another objective of the present disclosure is to minimize operational complexity by creating a solution that functions with existing medical imaging technologies, reducing the need for specialized hardware and simplifying adoption.
[0017] Another objective of the present disclosure is to incorporate a feature extraction process tailored for dermatology, allowing the model to capture specific indicators of melanoma that general-purpose models overlook.
[0018] Another objective of the present disclosure is to ensure cost-effective melanoma detection solutions that improve accessibility, particularly in low-resource regions where healthcare resources and dermatology specialists are limited.
[0019] Yet another objective of the present disclosure is to enhance interoperability with broader healthcare systems, allowing the melanoma detection model to seamlessly integrate with electronic medical records and other diagnostic tools.
[0020] Yet another objective of the present disclosure is to improve the robustness of melanoma detection through continuous learning and adaptability, enabling the model to evolve with emerging dermatological knowledge and imaging standards.
[0021] In light of the above, in one aspect of the present disclosure, an automated skin cancer detection system and method thereof is disclosed herein. The system comprises a dermatoscopic camera configured to capture high-resolution dermatoscopic images of skin lesions. The system includesa preprocessing unit operatively connected to the dermatoscopic camera configured for employing image preprocessing techniques including contrast enhancement, rotation, scaling, and flipping to standardize and augment dermatoscopic image data for training and analysis. The system also includes a feature extraction module integrated within the preprocessing unit configured to extract essential features such as edges, textures, asymmetries, and color variations. The system also includesa deep learning computational unit operatively connected to the preprocessing unit configured with various pre-trained deep learning models to achieve the highest precision and accuracy of 96.34 percent through enhanced attention mechanisms and residual connections. The system also includesa classification module integrated within the deep learning computational unit configured for employing transfer learning techniques and hyperparameter tuning to classify dermatoscopic images into melanoma and non-melanoma categories based on extracted features and training data. The system also includesan evaluation and validation module integrated within the deep learning computational unit configured for utilizing evaluation metrics including accuracy, recall, precision, receiver operating characteristic-area under the curve (ROC-AUC), and precision-recall- area under the curve (PR-AUC) for performance assessment and validation of the classification results. The system also includesa data storage and management unit operatively connected to the preprocessing unit and the deep learning computational unit configured to securely store annotated datasets comprising 44,126 dermatoscopic images of skin lesions from 3,000 patients and supports efficient retrieval for training, testing, and future analysis. The system also includesa visualization and feedback interface connected to the deep learning computational unit and the data storage and management unit configured for providing real-time display of classification outcomes, confusion matrices, and statistical evaluation metrics.
[0022] In one embodiment, the preprocessing unit is further configured to normalize the captured dermatoscopic images to a standardized format.
[0023] In one embodiment, the feature extraction module utilizes Gabor filters and thresholding techniques to refine the identification of regions of interest within the dermatoscopic images.
[0024] In one embodiment, the deep learning computational unit is further configured with VGG19 architecture enhanced with attention mechanisms and residual connections.
[0025] In one embodiment, the classification module employs ensemble learning techniques to combine outputs from multiple pre-trained models.
[0026] In one embodiment, the evaluation and validation module is further configured to generate confusion matrices and detailed statistical reports.
[0027] In one embodiment, the data storage and management unit is further configured to implement encryption protocols to ensure secure storage and retrieval of patient image data.
[0028] In one embodiment, the visualization and feedback interface provide an interactive platform for healthcare professionals to annotate images and provide feedback for model retraining.
[0029] In one embodiment, the preprocessing unit incorporates artifact removal techniques to eliminate noise and distortions in dermatoscopic images before feature extraction.
[0030] In light of the above, in one aspect of the present disclosure, an automated skin cancer detection system and method thereof is disclosed herein. The method comprising capturing dermatoscopic images using a dermatoscopic camera configured to obtain high-resolution images of skin lesions for analysis. The method includes preprocessing the dermatoscopic images by employing a preprocessing unit operatively connected to the dermatoscopic camera. The method also includes extracting essential features from the preprocessed images using a feature extraction module integrated within the preprocessing unit. The method also includes classifying the dermatoscopic images into melanoma and non-melanoma categories using a deep learning computational unit operatively connected to the preprocessing unit. The method also includes evaluating the classification performance by employing an evaluation and validation module integrated within the deep learning computational unit. The method also includes storing and managing the dermatoscopic data using a data storage and management unit operatively connected to the preprocessing unit and deep learning computational unit. The method also includes visualizing and providing feedback on classification outcomes using a visualization and feedback interface operatively connected to the deep learning computational unit and data storage unit.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an automated skin cancer detection system and method thereof, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flowchart of an automated skin cancer detection system, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates a flowchart of a method for automated detection of skin cancer using an image classification system, in accordance with an exemplary embodiment of the present disclosure;
[0039] FIG. 4A illustrates a perspective view of the melanoma images, in accordance with an exemplary embodiment of the present disclosure;
[0040] FIG. 4B illustrates a perspective view of the non-melanoma images, in accordance with an exemplary embodiment of the present disclosure;
[0041] FIG. 5 illustrates a perspective view of the methodology employed, in accordance with an exemplary embodiment of the present disclosure;
[0042] FIG. 6 illustrates a perspective view of the cancer and normal moles, in accordance with an exemplary embodiment of the present disclosure;
[0043] FIG. 7 illustrates a perspective view of the evaluation measures and precision, in accordance with an exemplary embodiment of the present disclosure;
[0044] FIG. 8A illustrates a perspective view of the accuracy comparison of models, in accordance with an exemplary embodiment of the present disclosure;
[0045] FIG. 8B illustrates a perspective view of the receiver operating characteristic-area under the curve (ROC-AUC) scores for different models, in accordance with an exemplary embodiment of the present disclosure;
[0046] FIG. 9A illustrates a perspective view of the comparison of precision-recall- area under the curve (PR-AUC) scores, in accordance with an exemplary embodiment of the present disclosure;
[0047] FIG. 9B illustrates a perspective view of the comparison of recall values, in accordance with an exemplary embodiment of the present disclosure;
[0048] FIG. 10A illustrates a perspective view of the gender distribution of non-melanoma and melanoma patients, in accordance with an exemplary embodiment of the present disclosure;
[0049] FIG. 10B illustrates a perspective view of the site distribution of non-melanoma and melanoma patients, in accordance with an exemplary embodiment of the present disclosure.
[0050] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0051] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0052] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0053] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0054] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0055] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0056] Referring now to FIG. 1 to FIG. 10 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a perspective view of an automated skin cancer detection system and method thereof 100, in accordance with an exemplary embodiment of the present disclosure.
[0057] The system 100 may include a dermatoscopic camera 102 configured to capture high-resolution dermatoscopic images of skin lesions, a preprocessing unit 104 operatively connected to the dermatoscopic camera 102 configured for employing image preprocessing techniques including contrast enhancement, rotation, scaling, and flipping to standardize and augment dermatoscopic image data for training and analysis, a feature extraction module 106 integrated within the preprocessing unit 104 configured to extract essential features such as edges, textures, asymmetries, and color variations, a deep learning computational unit 108 operatively connected to the preprocessing unit 104 configured with various pre-trained deep learning models to achieve the highest precision and accuracy of 96.34 percent through enhanced attention mechanisms and residual connections, a classification module 110 integrated within the deep learning computational unit 108 configured for employing transfer learning techniques and hyperparameter tuning to classify dermatoscopic images into melanoma and non-melanoma categories based on extracted features and training data, an evaluation and validation module 112 integrated within the deep learning computational unit 108 configured for utilizing evaluation metrics including accuracy, recall, precision, receiver operating characteristic-area under the curve (ROC-AUC), and precision-recall- area under the curve (PR-AUC) for performance assessment and validation of the classification results, a data storage and management unit 114 operatively connected to the preprocessing unit 104 and the deep learning computational unit 108 configured to securely store annotated datasets comprising 44,126 dermatoscopic images of skin lesions from 3,000 patients and supports efficient retrieval for training, testing, and future analysis, a visualization and feedback interface 116 connected to the deep learning computational unit 108 and the data storage and management unit 114 configured for providing real-time display of classification outcomes, confusion matrices, and statistical evaluation metrics.
[0058] The preprocessing unit 104 operatively connected to the dermatoscopic camera 102, is further configured to normalize the captured dermatoscopic images to a standardized format.
[0059] The feature extraction module 106 integrated within the preprocessing unit 104 utilizes gabor filters and thresholding techniques to refine the identification of regions of interest within the dermatoscopic images.
[0060] The deep learning computational unit 108 operatively connected to the preprocessing unit 104 is further configured with VGG19 architecture enhanced with attention mechanisms and residual connections.
[0061] The classification module 110 integrated within the deep learning computational unit 108 employs ensemble learning techniques to combine outputs from multiple pre-trained models.
[0062] The evaluation and validation module 112 integrated within the deep learning computational unit 108 is further configured to generate confusion matrices and detailed statistical reports.
[0063] The data storage and management unit 114 operatively connected to the preprocessing unit 104 and the deep learning computational unit 108 is further configured to implement encryption protocols to ensure secure storage and retrieval of patient image data.
[0064] The visualization and feedback interface 116 connected to the deep learning computational unit 108 and the data storage and management unit 114, provide an interactive platform for healthcare professionals to annotate images and provide feedback for model retraining.
[0065] The preprocessing unit 104 operatively connected to the dermatoscopic camera 102, incorporates artifact removal techniques to eliminate noise and distortions in dermatoscopic images before feature extraction.
[0066] The method 100 may include capturing dermatoscopic images using a dermatoscopic camera 102 configured to obtain high-resolution images of skin lesions for analysis, preprocessing the dermatoscopic images by employing a preprocessing unit 104 operatively connected to the dermatoscopic camera 102, extracting essential features from the preprocessed images using a feature extraction module 106 integrated within the preprocessing unit 104, classifying the dermatoscopic images into melanoma and non-melanoma categories using a deep learning computational unit 108 operatively connected to the preprocessing unit 104, evaluating the classification performance by employing an evaluation and validation module 112 integrated within the deep learning computational unit 108, storing and managing the dermatoscopic data using a data storage and management unit 114 operatively connected to the preprocessing unit 104 and deep learning computational unit 108, visualizing and providing feedback on classification outcomes using a visualization and feedback interface 116 operatively connected to the deep learning computational unit 108 and data storage unit.
[0067] The dermatoscopic camera 102 captures high-resolution dermatoscopic images of skin lesions, providing detailed visual data critical for accurate analysis. This specialized imaging hardware incorporates advanced optics and lighting systems that enhance image clarity and contrast, ensuring that even subtle patterns and textures are visible. By utilizing polarized and non-polarized lighting techniques, the dermatoscopic camera 102 enables effective visualization of subdermal structures, which are essential for distinguishing melanoma characteristics from benign features.
[0068] The dermatoscopic camera 102 employs high-resolution sensors that support the precise acquisition of image details, allowing for the detection of asymmetries, irregular borders, and colour variations. This functionality is critical in detecting melanoma features that might not be visible with standard imaging devices. Additionally, the dermatoscopic camera 102 integrates seamlessly with the preprocessing unit 104, ensuring that captured images are ready for further processing and analysis without requiring additional intermediate steps.
[0069] Through its ergonomic design, the dermatoscopic camera 102 facilitates ease of use for healthcare professionals, enabling consistent and reliable imaging under varying clinical conditions. The dermatoscopic camera 102 incorporates real-time image acquisition capabilities, which allow for immediate visualization of the skin lesions on connected displays. This facilitates on-the-spot verification of image quality and ensures that no essential details are missed.
[0070] The dermatoscopic camera 102 includes integrated connectivity features, allowing for direct transmission of captured images to the preprocessing unit 104. This connection eliminates potential delays and ensures efficient workflows, particularly in settings where large datasets need to be processed rapidly. By adhering to standardized imaging protocols, the dermatoscopic camera 102 ensures compatibility with the subsequent deep learning and classification modules of the system, maintaining the fidelity of critical data throughout the process.
[0071] The dermatoscopic camera 102, as the initial step in the automated skin cancer detection system, plays a vital role in enabling high-precision diagnosis and treatment planning by generating the foundational data required for deep learning analysis.
[0072] The preprocessing unit 104 is functioning as an integral part of the automated skin cancer detection system 100, designed to enhance the quality and usability of dermatoscopic images captured by the dermatoscopic camera 102. The preprocessing unit 104 is employing advanced image preprocessing techniques to standardize, normalize, and refine dermatoscopic images, ensuring uniformity across datasets and preparing the images for accurate feature extraction and classification.
[0073] The preprocessing unit 104 is actively utilizing contrast enhancement methods to improve the visibility of intricate details within dermatoscopic images. By highlighting edges, textures, and colour variations, the preprocessing unit 104 is facilitating the identification of melanoma-specific characteristics. Additionally, the preprocessing unit 104 is incorporating normalization techniques, standardizing the images into a uniform format to reduce inconsistencies arising from varying resolutions, lighting conditions, or imaging equipment.
[0074] The preprocessing unit 104 is applying data augmentation strategies, including image rotation, scaling, and flipping, to enrich the dataset. This process is expanding the variety of training samples and increasing the robustness and generalization capabilities of the deep learning computational unit 108. Furthermore, the preprocessing unit 104 is integrating artifact removal techniques to eliminate noise and distortions in dermatoscopic images, enhancing their clarity and ensuring the integrity of the data for subsequent analysis.
[0075] The preprocessing unit 104 is embedding a feature extraction module 106 within its framework to identify and isolate critical regions of interest in dermatoscopic images. By employing methods such as Gabor filters and thresholding techniques, the preprocessing unit 104 is enabling precise detection of edges, asymmetries, and texture patterns associated with melanoma. The preprocessing unit 104 is thereby ensuring a comprehensive preprocessing workflow, optimizing the dermatoscopic images for analysis by the deep learning computational unit 108. Through its advanced capabilities, the preprocessing unit 104 is establishing a solid foundation for accurate and efficient skin cancer detection.
[0076] The feature extraction module 106 is performing a crucial role in the automated skin cancer detection system 100 by systematically analyzing dermatoscopic images processed by the preprocessing unit 104. The feature extraction module 106 is operating as an intermediary layer, bridging raw image data and the deep learning computational unit 108 by identifying and isolating relevant visual and textural features that are indicative of melanoma or other skin conditions.
[0077] The feature extraction module 106 is actively employing a range of computational techniques to highlight distinct characteristics present within dermatoscopic images. The feature extraction module 106 is utilizing methods such as edge detection to outline the boundaries of lesions, enabling precise delineation of tumour edges and surrounding tissues. By detecting edges, the feature extraction module 106 is ensuring the accurate representation of lesion morphology, which is a critical parameter in melanoma analysis.
[0078] The feature extraction module 106 is further integrating texture analysis algorithms to evaluate surface patterns within the lesion areas. By employing advanced filters such as Gabor filters or wavelet transforms, the feature extraction module 106 is capturing the granularity, smoothness, and other micro-level surface properties. These texture-based attributes are enabling the feature extraction module 106 to differentiate between benign and malignant lesions with enhanced precision.
[0079] Additionally, the feature extraction module 106 is incorporating colour analysis methodologies to quantify colon distributions and variations within dermatoscopic images. Through segmentation and histogram analysis, the feature extraction module 106 is isolating distinct colour patterns such as shades of brown, black, white, or red, which are often linked to specific types of skin cancer. By leveraging these colour metrics, the feature extraction module 106 is enhancing the discriminatory power of the automated system.
[0080] The feature extraction module 106 is also employing shape analysis techniques to assess geometric attributes of lesions, such as asymmetry, border irregularity, and compactness. By computing shape descriptors like area, perimeter, and aspect ratio, the feature extraction module 106 is facilitating the accurate quantification of morphological anomalies associated with malignant lesions. These features are directly contributing to the diagnostic accuracy of the automated system.
[0081] Furthermore, the feature extraction module 106 is actively embedding segmentation techniques to isolate regions of interest within dermatoscopic images. By utilizing algorithms such as thresholding, clustering, or active contours, the feature extraction module 106 is ensuring that irrelevant background information is minimized, and the focus remains on clinically significant areas of the image. This process is enabling a refined feature set to be transmitted to the deep learning computational unit 108 for subsequent classification.
[0082] The feature extraction module 106 is incorporating feature selection mechanisms to optimize the dimensionality of the extracted data. By applying statistical and machine learning methods, the feature extraction module 106 is prioritizing the most relevant features, ensuring that computational resources are efficiently utilized while maintaining the integrity of diagnostic outcomes. This streamlined approach is enhancing the processing speed and accuracy of the skin cancer detection system.
[0083] Through its comprehensive analysis and advanced techniques, the feature extraction module 106 is functioning as a cornerstone of the automated skin cancer detection system 100. The feature extraction module 106 is enabling robust and precise data extraction, facilitating the subsequent stages of deep learning classification within the system.
[0084] The deep learning computational unit 108 is performing as the analytical core of the automated skin cancer detection system 100. The deep learning computational unit 108 is utilizing advanced algorithms based on deep learning frameworks to interpret the extracted features received from the feature extraction module 106. By processing these features, the deep learning computational unit 108 is generating predictions regarding the classification of dermatoscopic images into malignant or benign categories.
[0085] The deep learning computational unit 108 is operating by employing neural network architectures, such as convolutional neural networks or recurrent neural networks, to analyse complex patterns within the input data. The deep learning computational unit 108 is training its algorithms using a pre-annotated dataset of dermatoscopic images, ensuring that the neural networks learn to identify subtle visual cues and variations associated with different types of skin cancer. This training phase is enabling the deep learning computational unit 108 to acquire the necessary knowledge for accurate classification.
[0086] The deep learning computational unit 108 is implementing multiple layers of interconnected neurons, where each layer is progressively extracting more abstract features. The initial layers of the deep learning computational unit 108 are focusing on basic patterns such as edges, shapes, and textures, while deeper layers are identifying complex features like lesion irregularities, asymmetry, and unique colour patterns. This hierarchical approach is allowing the deep learning computational unit 108 to achieve a comprehensive understanding of dermatoscopic images.
[0087] The deep learning computational unit 108 is actively leveraging backpropagation techniques to minimize errors during the training phase. By adjusting the weights and biases of the neural network, the deep learning computational unit 108 is refining its predictive accuracy. This iterative process is ensuring that the deep learning computational unit 108 is adapting to diverse image variations and producing reliable diagnostic outcomes.
[0088] The deep learning computational unit 108 is incorporating dropout and regularization methods to prevent overfitting. By introducing these techniques, the deep learning computational unit 108 is maintaining a balance between model complexity and generalization capability. This balance is ensuring that the deep learning computational unit 108 is performing effectively on new and unseen dermatoscopic images.
[0089] The deep learning computational unit 108 is integrating ensemble learning methods to enhance decision-making robustness. By combining the outputs of multiple neural networks, the deep learning computational unit 108 is reducing the influence of any individual model's biases, thereby producing more consistent and accurate predictions. This integration is further strengthening the reliability of the automated skin cancer detection system 100.
[0090] The deep learning computational unit 108 is executing real-time processing of dermatoscopic images, ensuring that diagnostic results are generated promptly. By utilizing high-performance computational resources, the deep learning computational unit 108 is achieving low latency in data processing, which is crucial for timely medical interventions.
[0091] Through its intricate design and advanced learning methodologies, the deep learning computational unit 108 is serving as the intelligent engine of the automated skin cancer detection system 100. The deep learning computational unit 108 is enabling precise and reliable classification of dermatoscopic images, contributing significantly to the early detection and management of skin cancer.
[0092] The classification module 110 is functioning as the decisive layer of the automated skin cancer detection system 100. The classification module 110 is receiving processed data outputs from the deep learning computational unit 108 and is categorizing the data into predefined classes. By performing this classification, the classification module 110 is determining whether the analysed dermatoscopic image corresponds to a malignant or benign skin lesion.
[0093] The classification module 110 is operating based on probabilistic outputs generated by the deep learning computational unit 108. These outputs are representing the likelihood of the image belonging to various classes. The classification module 110 is analysing these probabilities and assigning the image to the class with the highest probability. By performing this task, the classification module 110 is simplifying the interpretation of the complex data processed earlier in the pipeline.
[0094] The classification module 110 is employing decision thresholds to enhance the accuracy of its predictions. These thresholds are ensuring that the classification module 110 is making decisions with a high degree of confidence, minimizing the risk of incorrect diagnosis. The classification module 110 is actively adjusting these thresholds based on predefined metrics, such as sensitivity and specificity, which are tailored to the clinical requirements of the automated skin cancer detection system 100.
[0095] The classification module 110 is integrating validation mechanisms to ensure the consistency and reliability of its output. These mechanisms are cross-verifying the classification results against a known dataset or additional computational checks. Through these validations, the classification module 110 is enhancing the trustworthiness of the automated skin cancer detection system 100.
[0096] The classification module 110 is implementing techniques to interpret and visualize its decision-making process. By providing interpretable results, the classification module 110 is enabling healthcare professionals to understand the rationale behind the classification, thereby aiding in informed clinical decision-making. The classification module 110 is also offering insights into the specific features or patterns that influenced the final classification.
[0097] The classification module 110 is facilitating continuous learning and updating of the automated skin cancer detection system 100. Through periodic re-training or model updates, the classification module 110 is ensuring that the system adapts to evolving medical knowledge, new datasets, or technological advancements. This adaptability is maintaining the relevance and effectiveness of the classification module 110 in real-world applications.
[0098] The classification module 110 is generating outputs that are not only accurate but also actionable. By categorizing lesions into malignant or benign, the classification module 110 is supporting early diagnosis and appropriate intervention strategies for skin cancer. The classification module 110 is contributing directly to reducing diagnostic errors and improving patient outcomes.
[0099] By maintaining seamless communication with other components of the automated skin cancer detection system 100, the classification module 110 is ensuring the integrity and efficiency of the entire diagnostic process. Through its precision, interpretability, and reliability, the classification module 110 is playing a pivotal role in transforming dermatoscopic image analysis into a clinically valuable tool. The classification module 110 is thereby advancing the goal of early detection and management of skin cancer.
[0100] The evaluation and validation module 112 is functioning as a critical component of the automated skin cancer detection system 100, ensuring the accuracy and reliability of the entire diagnostic process. The evaluation and validation module 112 is actively assessing the performance of the classification module 110 and other preceding components by comparing their outputs with known ground truth data. Through this process, the evaluation and validation module 112 is establishing the credibility of the system's diagnostic results.
[0101] The evaluation and validation module 112 is utilizing metrics such as accuracy, sensitivity, specificity, precision, and recall to quantify the performance of the automated skin cancer detection system 100. By calculating these metrics, the evaluation and validation module 112 is enabling a comprehensive assessment of the system's strengths and areas for improvement. The evaluation and validation module 112 is systematically analysing false positives and false negatives, ensuring a balanced understanding of the system's diagnostic capabilities.
[0102] The evaluation and validation module 112 is incorporating cross-validation techniques to ensure that the performance metrics are robust and generalizable across different datasets. By partitioning the dataset into training and testing subsets, the evaluation and validation module 112 is simulating real-world scenarios where the system is exposed to previously unseen data. This approach is ensuring that the evaluation and validation module 112 accurately reflects the reliability of the automated skin cancer detection system 100 in clinical settings.
[0103] The evaluation and validation module 112 is engaging in periodic benchmarking against external standards or reference datasets. By comparing the system's performance to state-of-the-art methodologies, the evaluation and validation module 112 is affirming the system's position within the landscape of skin cancer diagnostic technologies. This benchmarking is also providing insights for continuous improvement of the automated skin cancer detection system 100.
[0104] The evaluation and validation module 112 is performing real-time monitoring of the automated skin cancer detection system 100 during its operation. By analysing system outputs in real-time, the evaluation and validation module 112 is detecting anomalies or deviations from expected performance, which could indicate errors or inefficiencies in the system. The evaluation and validation module 112 is enabling immediate corrective actions to maintain the system's reliability.
[0105] The evaluation and validation module 112 is incorporating user feedback and clinical observations to validate the practical applicability of the automated skin cancer detection system 100. By integrating qualitative insights with quantitative evaluations, the evaluation and validation module 112 is ensuring that the system aligns with clinical requirements and user expectations. This iterative validation process is enhancing the system's adaptability to diverse medical environments.
[0106] The evaluation and validation module 112 is providing detailed reports and visualizations of its findings. These outputs are assisting healthcare professionals, researchers, and developers in understanding the system's performance and identifying opportunities for optimization. The evaluation and validation module 112 is contributing to transparent and evidence-based decision-making within the automated skin cancer detection system 100.
[0107] The evaluation and validation module 112 is acting as a bridge between technical development and clinical implementation. By rigorously validating the system's outputs and facilitating continuous improvement, the evaluation and validation module 112 is playing an essential role in establishing the automated skin cancer detection system 100 as a trustworthy and effective diagnostic tool in the fight against skin cancer.
[0108] The data storage and management unit 114 is functioning as a fundamental component of the automated skin cancer detection system 100, ensuring that all generated data is securely stored, organized, and readily accessible for subsequent processing and analysis. The data storage and management unit 114 is actively managing an extensive range of data types, including raw input data, intermediate results from various modules, and final diagnostic outputs, in a structured and efficient manner.
[0109] The data storage and management unit 114 is employing advanced database management systems to organize the vast volumes of data generated throughout the operation of the automated skin cancer detection system 100. By categorizing and indexing the data systematically, the data storage and management unit 114 is ensuring seamless retrieval and efficient processing by other modules, such as the classification module 110 and the evaluation and validation module 112.
[0110] The data storage and management unit 114 is incorporating robust data encryption and security protocols to protect sensitive medical and patient information. By implementing these measures, the data storage and management unit 114 is safeguarding the integrity and confidentiality of the stored data, aligning with legal and ethical standards, including compliance with health data protection regulations. This ensures that the automated skin cancer detection system 100 adheres to stringent privacy requirements.
[0111] The data storage and management unit 114 is actively managing data versioning to maintain a record of changes and updates to stored information. This functionality is facilitating traceability and reproducibility of results, allowing for detailed audits and validation of the automated skin cancer detection system 100. By preserving historical data alongside current records, the data storage and management unit 114 is enhancing the system's accountability.
[0112] The data storage and management unit 114 is integrating automated backup systems to prevent data loss due to unforeseen circumstances such as hardware failures or power interruptions. These backup mechanisms are operating continuously to ensure data redundancy and recovery, contributing to the overall reliability and resilience of the automated skin cancer detection system 100.
[0113] The data storage and management unit 114 is actively supporting data interoperability by employing standardized formats and interfaces. By ensuring compatibility with external databases, research systems, or clinical platforms, the data storage and management unit 114 is facilitating collaborative research and the integration of the automated skin cancer detection system 100 into existing medical infrastructures. This adaptability enhances the utility of the system in real-world scenarios.
[0114] The data storage and management unit 114 is optimizing data retrieval speeds through indexing and caching strategies. These enhancements are ensuring that other modules, such as the feature extraction module 106 and the deep learning computational unit 108, access necessary data without delays, thereby improving the overall operational efficiency of the automated skin cancer detection system 100.
[0115] The data storage and management unit 114 is providing a user-friendly interface for healthcare professionals and researchers to access, query, and analyse stored data. By presenting data in an organized and comprehensible format, the data storage and management unit 114 is empowering users to make informed decisions based on the results generated by the automated skin cancer detection system 100. This user-centric design promotes greater utility and engagement with the system's capabilities.
[0116] The visualization and feedback interface 116 is operating as a crucial element within the automated skin cancer detection system 100, providing an interactive platform for presenting results, analyses, and actionable insights. The visualization and feedback interface 116 is enhancing the user experience by translating complex diagnostic data into easily understandable visual formats, facilitating informed decision-making for healthcare professionals and users.
[0117] The visualization and feedback interface 116 is actively generating graphical representations of diagnostic outcomes, including charts, heatmaps, and annotated skin lesion images. These visual elements are allowing users to interpret the system's findings more intuitively, highlighting key features extracted by the feature extraction module 106 and the classification module 110. By displaying data in a clear and concise manner, the visualization and feedback interface 116 is reducing the cognitive load on users.
[0118] The visualization and feedback interface 116 is incorporating real-time interaction capabilities, enabling users to explore diagnostic data dynamically. Users are engaging with detailed views, zoomable lesion images, and layered visualizations that showcase multiple diagnostic parameters simultaneously. These features are supporting a comprehensive understanding of the analysed data and the automated skin cancer detection system 100's predictive reasoning.
[0119] The visualization and feedback interface 116 is presenting feedback on the performance of the automated skin cancer detection system 100 through metrics such as accuracy, sensitivity, and specificity, as generated by the evaluation and validation module 112. These metrics are offering users transparency into the reliability of the system and promoting trust in its diagnostic capabilities.
[0120] The visualization and feedback interface 116 is integrating color-coded systems and visual markers to emphasize critical information, such as areas of concern on skin lesion images or thresholds indicating potential malignancy. This approach is drawing the user's attention to high-priority data, ensuring timely and effective responses to diagnostic outcomes.
[0121] The visualization and feedback interface 116 is offering customization options, enabling users to tailor the presentation of diagnostic information to meet their specific needs. Users are adjusting layouts, selecting preferred visualization styles, and prioritizing data points based on their roles and responsibilities. This adaptability is enhancing the usability of the automated skin cancer detection system 100 across various healthcare contexts.
[0122] The visualization and feedback interface 116 is facilitating integration with external devices and platforms, such as electronic health records and telemedicine systems. By supporting data sharing and interoperability, the visualization and feedback interface 116 is promoting seamless collaboration between healthcare professionals and expanding the reach of the automated skin cancer detection system 100.
[0123] The visualization and feedback interface 116 is continuously receiving user input and adapting its design and functionalities to meet evolving user requirements. This feedback loop is ensuring that the visualization and feedback interface 116 remains responsive to user preferences and clinical needs, contributing to the ongoing refinement of the automated skin cancer detection system 100.
[0124] The visualization and feedback interface 116 is incorporating accessibility features, such as adjustable font sizes, screen reader compatibility, and multilingual support, to accommodate diverse user groups. By prioritizing inclusivity, the visualization and feedback interface 116 is ensuring that the automated skin cancer detection system 100 is accessible to a broad audience, including users with varying levels of technical proficiency or physical abilities.
[0125] FIG. 2 illustrates a flowchart of an automated skin cancer detection system, in accordance with an exemplary embodiment of the present disclosure.
[0126] At 202, a dermatoscopic camera captures high-resolution images of skin lesions.
[0127] At 204, the captured images undergo preprocessing, which includes techniques like contrast enhancement, rotation, scaling, and flipping to standardize and augment the data.
[0128] At 206, essential features such as edges, textures, asymmetries, and color variations are extracted from the pre-processed images.
[0129] At 208, the extracted features are then fed into a deep learning computational unit, which employs pre-trained deep learning models like VGG19. These models are further enhanced with techniques like attention mechanisms and residual connections to achieve high accuracy and precision.
[0130] At 210, the deep learning model classifies the skin lesions into either malignant (melanoma) or benign (non-melanoma) categories based on the extracted features and the learned patterns.
[0131] At 212, the system evaluates its performance using metrics like accuracy, precision, recall, receiver operating characteristic-area under the curve (ROC-AUC), and precision-recall- area under the curve (PR-AUC).
[0132] At 214, a comprehensive dataset of 44,126 dermoscopic images is stored and managed for future training, testing, and analysis.
[0133] At 216, the system provides a user-friendly interface to display classification results, confusion matrices, and statistical evaluation metrics.
[0134] FIG. 3 illustrates a flowchart of a method for automated detection of skin cancer using an image classification system, in accordance with an exemplary embodiment of the present disclosure.
[0135] At 302, capture dermatoscopic images using a dermatoscopic camera configured to obtain high-resolution images of skin lesions for analysis.
[0136] At 304, preprocess the dermatoscopic images by employing a preprocessing unit operatively connected to the dermatoscopic camera.
[0137] At 306, extract essential features from the pre-processed images using a feature extraction module integrated within the preprocessing unit.
[0138] At 308, classify the dermatoscopic images into melanoma and non-melanoma categories using a deep learning computational unit operatively connected to the preprocessing unit.
[0139] At 310, evaluate the classification performance by employing an evaluation and validation module integrated within the deep learning computational unit.
[0140] At 312, store and manage the dermatoscopic data using a data storage and management unit operatively connected to the preprocessing unit and deep learning computational unit.
[0141] At 314, visualize and provide feedback on classification outcomes using a visualization and feedback interface operatively connected to the deep learning computational unit and data storage unit.
[0142] FIG. 4A illustrates a perspective view of melanoma images in accordance with an exemplary embodiment of the present disclosure. The melanoma images presented in FIG. 4A are captured using the dermatoscopic camera 102, which is specifically configured to obtain high-resolution dermatoscopic images of skin lesions for detailed analysis. These melanoma images demonstrate distinct visual characteristics, including asymmetry, irregular borders, colour variations, and changes in size, essential for distinguishing melanoma from non-melanoma lesions.
[0143] The preprocessing unit 104 processes the melanoma images captured by the dermatoscopic camera 102 to standardize the image data. The preprocessing techniques employed by the preprocessing unit 104 include contrast enhancement, scaling, rotation, flipping, normalization, and artifact removal to ensure the images are suitable for further analysis. These preprocessing steps ensure that all visual details in the melanoma images are highlighted effectively for feature extraction.
[0144] The feature extraction module 106 integrated within the preprocessing unit 104 analyses the melanoma images to extract critical features such as edges, textures, asymmetries, and specific colour patterns associated with melanoma. This process enables the deep learning computational unit 108 to utilize these extracted features for accurate classification. The melanoma images depicted in FIG. 4A represent a crucial dataset for training and validating the automated skin cancer detection system 100.
[0145] FIG. 4B illustrates a perspective view of the non-melanoma images, in accordance with an exemplary embodiment of the present disclosure. The dermatoscopic camera 102 captures high-resolution images of skin lesions that do not exhibit melanoma characteristics. These non-melanoma images display typical dermatoscopic features, including symmetrical shapes, consistent pigmentation, and regular borders, which distinguish them from malignant melanoma lesions. The dermatoscopic camera 102 provides these detailed images to enable precise analysis and differentiation.
[0146] The preprocessing unit 104 connected to the dermatoscopic camera 102 performs preprocessing on the non-melanoma images. The preprocessing unit 104 standardizes the image data through contrast enhancement, rotation, scaling, flipping, and normalization techniques, ensuring that all non-melanoma images are uniform and free from distortions or artifacts. The preprocessing unit 104 prepares these images for further analysis.
[0147] The feature extraction module 106, integrated within the preprocessing unit 104, identifies and extracts specific features of non-melanoma lesions, such as smooth edges, uniform textures, and colour patterns. The feature extraction module 106 utilizes techniques like Gabor filters and thresholding to refine the identification of these features, which are critical for distinguishing non-melanoma images from melanoma cases.
[0148] The deep learning computational unit 108 receives the extracted features and analyses them using pre-trained models, including VGG19 architecture with enhanced attention mechanisms and residual connections. The classification module 110 within the deep learning computational unit 108 categorizes the pre-processed images as non-melanoma based on their distinct features.
[0149] FIG. 5 illustrates a perspective view of the methodology employed, in accordance with an exemplary embodiment of the present disclosure.
[0150] The dataset preparation 502 step is focusing on gathering, organizing, and curating relevant skin lesion datasets to train and evaluate the automated skin cancer detection system 100. Dataset preparation 502 is ensuring the inclusion of diverse and representative samples, encompassing different skin types, lesion types, and diagnostic categories to promote robustness and generalizability in the automated system's outputs.
[0151] Dataset preparation 502 is involving processes such as data annotation, were medical experts label images with corresponding diagnostic information. Additionally, dataset preparation 502 is involving balancing the dataset to address class imbalances, enhancing the reliability of the subsequent steps like image preprocessing 504 and deep learning model selection 508.
[0152] The image preprocessing 504 step is focusing on enhancing the quality of input images to ensure that they are suitable for subsequent analysis in the automated system. Image preprocessing 504 is performing operations such as noise removal, resizing, and normalization to standardize the dataset prepared during the dataset preparation 502 step.
[0153] Image preprocessing 504 is also including techniques like contrast adjustment to highlight relevant features and artifact removal to eliminate unwanted elements that may distort analysis. By refining image quality, image preprocessing 504 is ensuring that feature extraction and segmentation 506 operate on optimized data, facilitating accurate system performance.
[0154] The feature extraction and segmentation 506 step is focusing on isolating relevant patterns and sections from the pre-processed images generated in the image preprocessing 504 step. This step is identifying critical features such as edges, shapes, textures, or specific regions of interest that are crucial for further analysis.
[0155] Feature extraction and segmentation 506 is using advanced algorithms to segment the images into distinct parts, ensuring that each part is representing meaningful information. By refining and isolating the features, feature extraction and segmentation 506 is preparing data for deep learning model selection 508, enabling precise and effective computational learning processes.
[0156] The deep learning model selection 508 step is focusing on identifying the most suitable neural network architecture to process the extracted features from the feature extraction and segmentation 506 step. This step is evaluating various deep learning models to determine which model aligns best with the nature of the data and the desired outcomes.
[0157] Deep learning model selection 508 is conducting experiments with different configurations, hyperparameters, and training methodologies to optimize the model's performance. By carefully choosing the right model, deep learning model selection 508 is ensuring accurate classification and analysis, which subsequently feeds into evaluation metrics 510 for validation and performance assessment.
[0158] The evaluation metrics 510 step is assessing the performance and effectiveness of the selected deep learning model from the deep learning model selection 508 step. This step is employing various quantitative metrics, such as accuracy, precision, recall, and F1 score, to analyse the model's ability to correctly classify and predict outcomes based on the processed data.
[0159] Evaluation metrics 510 is systematically comparing the predicted outputs to the actual results obtained from the feature extraction and segmentation 506 step. By interpreting these metrics, evaluation metrics 510 is validating the robustness and reliability of the entire process, ensuring that the objectives set during dataset preparation 502 are being met.
[0160] FIG. 6 illustrates a perspective view of the cancerous and normal moles, in accordance with an exemplary embodiment of the present disclosure. The dermatoscopic camera 102 captures high-quality images of moles, including cancerous and non-cancerous cases, providing detailed visual information for accurate diagnosis. The dermatoscopic camera 102 ensures that features like asymmetry, irregular borders, colour variations, and size differences are distinctly visible in the captured images.
[0161] The preprocessing unit 104 connected to the dermatoscopic camera 102 processes the raw images by applying image enhancement techniques. The preprocessing unit 104 enhances the contrast, eliminates noise, and normalizes the images to achieve a consistent quality for both cancerous and normal moles. This preprocessing step ensures uniformity and prepares the data for detailed analysis.
[0162] The feature extraction module 106 within the preprocessing unit 104 extracts critical features from the images. For cancerous moles, the feature extraction module 106 identifies characteristics such as asymmetry, uneven pigmentation, and irregular patterns. For normal moles, the feature extraction module 106 focuses on features like symmetry, smooth borders, and consistent colour distribution.
[0163] The deep learning computational unit 108 processes the extracted features using advanced neural network architectures. The classification module 110 within the deep learning computational unit 108 distinguishes cancerous moles from normal moles based on their extracted features and assigns appropriate labels for further diagnostic evaluation.
[0164] Figure 7 illustrates a perspective view of the evaluation measures and precision, highlighting the performance of the system in classifying skin lesions into melanoma and non-melanoma categories. In this embodiment, the evaluation and validation module 112 plays a pivotal role by assessing the accuracy and precision of the deep learning model's classification outcomes. The module 112 utilizes several key metrics, including recall, precision, and accuracy, to determine the effectiveness of the automated skin cancer detection process. Additionally, the receiver operating characteristic-area under the curve (ROC-AUC) and precision-recall-area under the curve (PR-AUC) scores provide further insights into the model's discriminative power, indicating how well the system distinguishes between cancerous and non-cancerous lesions.
[0165] The results of the evaluation measures are displayed through a detailed graphical representation, where each performance metric is compared across various deep learning models. The classification module 110, integrated within the deep learning computational unit 108, is responsible for categorizing the dermatoscopic images based on these evaluation metrics. The accuracy comparison emphasizes the performance improvements achieved through the utilization of enhanced architectures, such as VGG19, which delivers the highest precision in melanoma detection.
[0166] Figure 8A illustrates a perspective view of the accuracy comparison of models, providing a comprehensive analysis of the performance across various deep learning models used for skin cancer detection. In this embodiment, the accuracy of each model is evaluated based on its ability to correctly classify dermatoscopic images into melanoma and non-melanoma categories. The classification module 110, integrated within the deep learning computational unit 108, plays a key role in determining the precision of each model's output. This comparison highlights how different architectures, such as VGG16-based convolutional neural network (CNN), simplified CNN, AlexNet, VGG19, ResNet50, and MobileNet, perform under similar conditions and dataset parameters.
[0167] The evaluation and validation module 112 further contributes by assessing the results from each model using key performance indicators like recall, precision, and accuracy. VGG19, which is configured with attention mechanisms and residual connections, consistently achieves the highest level of accuracy. This comparison underscores the importance of architectural enhancements in improving the precision of melanoma detection. The analysis allows for a clear understanding of how different models perform relative to one another and emphasizes the superiority of certain configurations in achieving optimal diagnostic results.
[0168] Figure 8B illustrates a perspective view of the receiver operating characteristic-area under the curve (ROC-AUC) scores for different models, providing a detailed comparison of the models' diagnostic abilities. The receiver operating characteristic-area under the curve (ROC-AUC) score evaluates the performance of various deep learning models in distinguishing between melanoma and non-melanoma skin lesions. This evaluation method measures the true positive rate versus the false positive rate, with a higher AUC indicating better model performance.
[0169] The classification module 110 contributes significantly to the analysis by generating these receivers operating characteristic (ROC) curves for each model, including VGG16-based convolutional neural network (CNN), simplified convolutional neural network (CNN), AlexNet, VGG19, ResNet50, and MobileNet. Each of these models is tested on a consistent set of dermatoscopic images, allowing for a fair comparison of their ability to correctly classify the images as melanoma or non-melanoma.
[0170] The receiver operating characteristic-area under the curve (ROC-AUC) scores provide valuable insight into the diagnostic capabilities of each model, highlighting the areas where certain architectures outperform others. VGG19, which is equipped with advanced features such as attention mechanisms and residual connections, demonstrates superior performance in terms of AUC. The receiver operating characteristic analysis reveals how each model balances sensitivity and specificity, offering a comprehensive understanding of their overall efficacy in clinical skin cancer detection.
[0171] Figure 9A illustrates a perspective view of the comparison of precision-recall-area under the curve (PR-AUC) scores, showcasing the performance of different deep learning models in classifying melanoma and non-melanoma skin lesions. The precision-recall area under the curve (PR-AUC) score is a critical metric used to assess a model's precision and recall, especially when dealing with imbalanced datasets. Precision measures the accuracy of the positive predictions, while recall evaluates the model's ability to correctly identify all positive instances.
[0172] The classification module 110 plays a crucial role in calculating these scores by evaluating each model's ability to differentiate between melanoma and non-melanoma lesions. Various models, including VGG16-based convolutional neural network (CNN), simplified CNN, AlexNet, VGG19, ResNet50, and MobileNet, are tested and compared on a standardized dataset of dermatoscopic images.
[0173] The precision-recall-area under the curve (PR-AUC) scores reveal how well each model maintains a balance between precision and recall, particularly in distinguishing melanoma skin lesions from non-melanoma ones. A higher precision-recall-area under the curve (PR-AUC) score signifies better performance, as it indicates a model's ability to identify melanoma lesions with both high accuracy and a low rate of false positives. VGG19, with its advanced architecture, shows notable performance, confirming its effectiveness in skin lesion classification.
[0174] Figure 9B illustrates a perspective view of the comparison of recall values, highlighting the performance of different deep learning models in accurately identifying melanoma skin lesions. Recall, also known as sensitivity, is a crucial metric for evaluating the model's ability to correctly identify all positive instances, in this case, melanoma lesions. High recall values indicate that the model successfully detects most of the melanoma lesions in the dataset, minimizing the number of false negatives.
[0175] The comparison involves various deep learning models, including VGG16-based convolutional neural network (CNN), simplified convolutional neural network (CNN), AlexNet, VGG19, ResNet50, and MobileNet. Each of these models is assessed for its recall in the task of melanoma detection. The classification module 110 processes the input dermatoscopic images and measures how well each model identifies melanoma skin lesions from non-melanoma ones.
[0176] Among the tested models, VGG19 demonstrates superior recall, signalling its strong performance in correctly classifying melanoma lesions. Higher recall values from these models reflect their ability to minimize missed melanoma detections, an essential factor in medical image analysis for skin cancer detection. The results show how different model architectures influence recall performance, with more advanced models like VGG19 showing a greater ability to detect melanoma lesions with fewer missed cases.
[0177] Figure 10A illustrates a perspective view of the gender distribution of non-melanoma and melanoma patients. This figure highlights the gender-specific breakdown in the dataset used for training and evaluating the deep learning models for melanoma detection. The gender distribution of both non-melanoma and melanoma patients is an important factor in understanding how well the model generalizes across different demographic groups.
[0178] The dataset includes a balanced representation of male and female patients with both non-melanoma and melanoma conditions. By analysing the gender distribution, the classification module 110 helps assess whether the models show any gender biases in their predictions. This is essential to ensure that the models perform equally well for both male and female patients, without favouring one group over the other.
[0179] The gender distribution is visually represented to highlight the proportion of male and female patients within each category, non-melanoma and melanoma. The deep learning models used in this study, including VGG16-based convolutional neural network (CNN), simplified CNN, AlexNet, VGG19, ResNet50, and MobileNet, process this data to accurately classify lesions based on gender-specific features while avoiding overfitting to gender-specific patterns.
[0180] This analysis aims to verify the fairness and robustness of the models across different gender groups, providing valuable insights into the models' reliability in real-world scenarios.
[0181] Figure 10B illustrates a perspective view of the site distribution of non-melanoma and melanoma patients. This figure showcases the anatomical distribution of lesions across different body sites in patients diagnosed with non-melanoma and melanoma. The site distribution provides valuable insight into the diversity of the dataset and helps evaluate how well the deep learning models generalize across various lesion locations.
[0182] The site distribution data, represented for both non-melanoma and melanoma patients, helps in understanding the prevalence of lesions on different body parts. This information is crucial for the model evaluation module 120 since certain body sites may have higher or lower incidences of melanoma, influencing the performance of the models. Different models, including the VGG16-based convolutional neural network (CNN), simplified CNN, AlexNet, VGG19, ResNet50, and MobileNet, are trained to identify and classify lesions from these varying anatomical sites.
[0183] By considering site-specific patterns in lesion characteristics, the models ensure robust performance across all locations. The inclusion of diverse lesion sites in the dataset ensures that the models do not overfit to specific body parts, but instead learn generalizable features for accurate melanoma detection. This analysis also helps to assess whether any site-related biases exist in the classification outcomes, further enhancing the model's clinical applicability.
[0184] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0185] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0186] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An automated skin cancer detection system (100), the system (100) comprises:
a dermatoscopic camera (102) configured to capture high-resolution dermatoscopic images of skin lesions;
a preprocessing unit (104) operatively connected to the dermatoscopic camera (102) configured for employing image preprocessing techniques including contrast enhancement, rotation, scaling, and flipping to standardize and augment dermatoscopic image data for training and analysis;
a feature extraction module (106) integrated within the preprocessing unit (104) configured to extract essential features such as edges, textures, asymmetries, and color variations;
a deep learning computational unit (108) operatively connected to the preprocessing unit (104) configured with various pre-trained deep learning models to achieve the highest precision and accuracy of 96.34 percent through enhanced attention mechanisms and residual connections;
a classification module (110) integrated within the deep learning computational unit (108) configured for employing transfer learning techniques and hyperparameter tuning to classify dermatoscopic images into melanoma and non-melanoma categories based on extracted features and training data;
an evaluation and validation module (112) integrated within the deep learning computational unit (108) configured for utilizing evaluation metrics including accuracy, recall, precision, receiver operating characteristic-area under the curve (ROC-AUC), and precision-recall- area under the curve (PR-AUC) for performance assessment and validation of the classification results;
a data storage and management unit (114) operatively connected to the preprocessing unit (104) and the deep learning computational unit (108) configured to securely store annotated datasets comprising 44,126 dermatoscopic images of skin lesions from 3,000 patients and supports efficient retrieval for training, testing, and future analysis;
a visualization and feedback interface (116) connected to the deep learning computational unit (108) and the data storage and management unit (114) configured for providing real-time display of classification outcomes, confusion matrices, and statistical evaluation metrics.
2. The system (100) as claimed in claim 1, wherein the preprocessing unit (104) operatively connected to the dermatoscopic camera (102), is further configured to normalize the captured dermatoscopic images to a standardized format.
3. The system (100) as claimed in claim 1, wherein the feature extraction module (106) integrated within the preprocessing unit (104) utilizes gabor filters and thresholding techniques to refine the identification of regions of interest within the dermatoscopic images.
4. The system (100) as claimed in claim 1, wherein the deep learning computational unit (108) operatively connected to the preprocessing unit (104) is further configured with VGG19 architecture enhanced with attention mechanisms and residual connections.
5. The system (100) as claimed in claim 1, wherein the classification module (110) integrated within the deep learning computational unit (108) employs ensemble learning techniques to combine outputs from multiple pre-trained models.
6. The system (100) as claimed in claim 1, wherein the evaluation and validation module (112) integrated within the deep learning computational unit (108) is further configured to generate confusion matrices and detailed statistical reports.
7. The system (100) as claimed in claim 1, wherein the data storage and management unit (114) operatively connected to the preprocessing unit (104) and the deep learning computational unit (108) is further configured to implement encryption protocols to ensure secure storage and retrieval of patient image data.
8. The system (100) claimed in claim 1, wherein the visualization and feedback interface (116) connected to the deep learning computational unit (108) and the data storage and management unit (114), provide an interactive platform for healthcare professionals to annotate images and provide feedback for model retraining.
9. The system (100) as claimed in claim 1, wherein the preprocessing unit (104) operatively connected to the dermatoscopic camera (102), incorporates artifact removal techniques to eliminate noise and distortions in dermatoscopic images before feature extraction.
10. A method for automated detection of skin cancer using an image classification system (100), the method (100) comprising:
capturing dermatoscopic images using a dermatoscopic camera (102) configured to obtain high-resolution images of skin lesions for analysis;
preprocessing the dermatoscopic images by employing a preprocessing unit (104) operatively connected to the dermatoscopic camera (102);
extracting essential features from the preprocessed images using a feature extraction module (106) integrated within the preprocessing unit (104);
classifying the dermatoscopic images into melanoma and non-melanoma categories using a deep learning computational unit (108) operatively connected to the preprocessing unit (104);
evaluating the classification performance by employing an evaluation and validation module (112) integrated within the deep learning computational unit (108);
storing and managing the dermatoscopic data using a data storage and management unit (114) operatively connected to the preprocessing unit (104) and deep learning computational unit (108);
visualizing and providing feedback on classification outcomes using a visualization and feedback interface (116) operatively connected to the deep learning computational unit (108) and data storage unit.

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