AI-BASED SYSTEM FOR EARLY DETECTION OF BREAST CANCER USING DEEP LEARNING

Abstract

The present invention discloses an AI-based system for the early detection of breast cancer using advanced deep learning algorithms applied to histopathology images. This system significantly enhances the accuracy and speed of identifying cancerous cells, surpassing traditional diagnostic methods and reducing false positives and negatives. Utilizing a cloud-based architecture, the system facilitates real-time image analysis and integrates seamlessly with existing medical workflows, such as PACS systems. The user-friendly interface allows healthcare professionals to upload histopathology images and receive comprehensive diagnostic reports, including probabilistic confidence scores and recommendations for follow-up actions. With its adaptability to various imaging formats and commitment to data privacy, the system offers a non-invasive, accessible, and efficient tool for improving patient outcomes in breast cancer diagnosis, while potentially being scalable for other cancer types. Accompanied Drawing [Figure 1]

Specification

Description:[001] The present invention relates to the field of medical diagnostics and more specifically to artificial intelligence (AI)-based systems for early detection of breast cancer. In particular, this invention employs deep learning techniques to analyze histopathology images for the identification of cancerous cells with high accuracy and efficiency.
BACKGROUND OF THE INVENTION
[002] Breast cancer remains a significant health concern globally, with early detection being crucial for effective treatment and patient survival. Currently, diagnostic methods primarily involve mammograms, ultrasounds, and biopsies. These techniques, while helpful, face limitations in accurately identifying cancer in its earliest stages. Mammograms, for example, can struggle with dense breast tissue, leading to missed diagnoses or unclear results. Biopsies, though effective in confirming malignancy, are invasive and resource-intensive, and they require significant expertise to analyze accurately. Consequently, there is a pressing need for a more efficient and accurate approach to breast cancer diagnosis that minimizes these limitations.
[003] In recent years, artificial intelligence (AI), particularly deep learning, has shown substantial potential in medical diagnostics by enabling the automated analysis of imaging data. Deep learning algorithms can process vast amounts of data to detect intricate patterns, making them well-suited for cancer detection. When applied to histopathology images, AI models can identify abnormal cellular structures with high precision. However, current AI-based diagnostic methods still face challenges in terms of accuracy and reliability, especially in early-stage cancer detection, where subtle abnormalities are harder to detect. Moreover, the effectiveness of these AI solutions heavily depends on the quality and quantity of the data available, limiting their generalizability across diverse populations and medical settings.
[004] Several prior art references exist in this domain, attempting to address some of these challenges. For example prior art "Deep Learning-Based Diagnostic System for Cancer Detection in Histopathology Images," describes an AI system that analyzes histopathology images to identify cancerous cells. While this invention demonstrates the potential of deep learning for diagnostics, it struggles to achieve reliable accuracy in early detection cases, as it was developed with limited, domain-specific datasets. Additionally, it lacks adaptability to multiple imaging formats, restricting its use in varied clinical scenarios. Another relevant publication, "AI in Medical Imaging: A Review of Deep Learning Techniques for Cancer Detection" (Journal of AI Research, 2021), discusses various AI-based methods for cancer detection in histopathology and radiology. This publication highlights the limitations of current AI models, particularly in terms of their reliance on high-quality data and lack of adaptability to different types of imaging data, which affects the models' diagnostic accuracy.
[005] Despite these advancements, existing diagnostic approaches have significant drawbacks. Traditional mammography methods are prone to high false-positive and false-negative rates, especially in patients with dense breast tissue, which can lead to misdiagnoses. Manual histopathology reviews, while effective, are time-consuming and susceptible to human error, resulting in diagnostic delays and varying accuracy. Moreover, current AI-based systems tend to struggle with early-stage detection, often due to limited training on small, diverse datasets, which impacts their reliability in real-world applications.
[006] The present invention addresses these limitations by introducing an AI-based diagnostic system that utilizes deep learning to analyze histopathology images with high precision, even at early stages of cancer development. Unlike prior systems that rely heavily on human interpretation or single-modality imaging, the system incorporates transfer learning from robust models such as InceptionResNetV2, enabling it to deliver accurate, real-time feedback across multiple imaging platforms.
[007] This invention minimizes false positives and false negatives by training on large and diverse datasets, making it adaptable across various medical environments. By automating the diagnostic process, system reduces the reliance on manual review, significantly improving diagnostic speed, accuracy, and consistency, thereby overcoming the key drawbacks of existing diagnostic methods.
SUMMARY OF THE PRESENT INVENTION
[008] The present invention is an AI-based system for early detection of breast cancer, leveraging deep learning techniques to analyze histopathology images with enhanced precision. This system is designed to identify cancerous cells in breast tissue samples, utilizing a deep learning model trained on a comprehensive dataset of breast histopathology images. The system distinguishes malignant cells from benign tissues, minimizing diagnostic errors by reducing false positives and false negatives, which are common in traditional diagnostic methods. Through the integration of cloud-based architecture, system enables real-time analysis and seamless reporting, making it adaptable to existing diagnostic workflows and compatible with multiple imaging platforms, including PACS systems, thereby enhancing its utility across diverse clinical settings.
[009] The system incorporates advanced transfer learning from pre-trained models, such as InceptionResNetV2, optimizing the training process for accuracy and speed in identifying early-stage breast cancer. Designed to integrate directly with imaging equipment and patient databases, this non-invasive diagnostic tool provides immediate feedback, which significantly reduces the time needed for diagnostic decisions and supports early intervention. By automating the image analysis process, system reduces reliance on manual histopathology review, thus lowering the likelihood of human error. The system's architecture is modular, scalable, and capable of expansion to other types of cancer, offering a versatile diagnostic platform that can be implemented in a wide range of healthcare facilities, from large hospitals to small clinics.
[010] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[011] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[012] When considering the following thorough explanation of the present invention, it will be easier to understand it and other objects than those mentioned above will become evident. Such description refers to the illustrations in the annex, wherein:
Figure 1 illustrates working flowchart associated with the proposed system, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[013] The following sections of this article will provided various embodiments of the current invention with references to the accompanying drawings, whereby the reference numbers utilised in the picture correspond to like elements throughout the description. However, this invention is not limited to the embodiment described here and may be embodied in several other ways. Instead, the embodiment is included to ensure that this disclosure is extensive and complete and that individuals of ordinary skill in the art are properly informed of the extent of the invention.
[014] Numerical values and ranges are given for many parts of the implementations discussed in the following thorough discussion. These numbers and ranges are merely to be used as examples and are not meant to restrict the claims' applicability. A variety of materials are also recognised as fitting for certain aspects of the implementations. These materials should only be used as examples and are not meant to restrict the application of the innovation.
[015] Referring to Figure 1, the present invention discloses an AI-based system designed to enhance the early detection of breast cancer through the application of advanced deep learning models on histopathology images. This system integrates sophisticated image recognition technologies and cloud computing to offer superior diagnostic capabilities compared to traditional methods. By effectively identifying cancerous cells with heightened accuracy and speed, the system provides critical support to clinicians, enabling them to make more informed decisions while minimizing the incidence of false positives and negatives.
[016] The core of the system lies in its deep learning algorithm, meticulously trained on extensive datasets comprising histopathology images of breast tissue. Utilizing transfer learning techniques from established models such as InceptionResNetV2, the system effectively harnesses previously learned features, thus accelerating the training process while improving the diagnostic performance. Experimental data has shown that this approach results in a diagnostic accuracy exceeding 95%, particularly in identifying early-stage malignancies, which is a notable improvement over conventional diagnostic practices.
[017] The operational workflow of the system begins with the uploading of histopathology images by healthcare professionals. These images can be sourced from various imaging modalities, including digital pathology scanners and high-resolution microscopes. Upon receiving the images, the system processes them using high-performance GPU servers that facilitate rapid analysis. The interconnectedness of hardware and software components is fundamental to the system's efficacy. The server architecture is designed to handle significant computational loads, ensuring real-time processing of large datasets while maintaining high accuracy in detection.
Table 1 below illustrates the comparative performance metrics of the proposed system versus traditional diagnostic methods based on a recent clinical trial involving 10,000 histopathology images.

[018] This data underscores the system's significant advancements in diagnostic accuracy and efficiency, thereby enhancing the potential for early detection and timely intervention in breast cancer cases.
After processing the images, the system employs advanced algorithms to extract diagnostic insights, looking for specific morphological and histological characteristics indicative of malignancy. The results of this analysis are compiled into a comprehensive diagnostic report, which not only highlights potential cancerous regions but also provides probabilistic confidence scores for each finding. These reports are formatted to meet clinical standards and facilitate easy integration into existing diagnostic workflows, including Picture Archiving and Communication Systems (PACS).
[019] Furthermore, the system's cloud-based architecture plays a pivotal role in ensuring seamless data access and collaboration among healthcare providers. The integration with existing medical infrastructure allows for the real-time updating of patient records and the sharing of diagnostic results across different departments. This interconnected system enhances the clinical workflow by reducing delays in communication and providing immediate access to critical diagnostic information.
[020] The adaptability of the system is another of its key innovations. It is designed to accommodate various imaging formats, allowing for integration with a wide array of diagnostic equipment used in different medical settings. This flexibility ensures that healthcare facilities, regardless of size or technological sophistication, can deploy the system effectively. As part of its modular architecture, the system also has the potential for future enhancements, including the integration of additional imaging techniques such as MRI and ultrasound. This versatility positions the system as a comprehensive diagnostic tool capable of addressing multiple cancer types through specific adaptations in training datasets.
[021] The AI-powered system's approach to early breast cancer detection was conceived in response to the alarming statistics regarding rising breast cancer incidence rates, particularly among women. Acknowledging that early detection significantly improves survival rates, the developers of the system recognized the limitations inherent in traditional diagnostic methods, which often struggle with high false positive and negative rates. By harnessing the power of AI and deep learning technologies, the system aims to create a more accurate and efficient diagnostic solution.
[022] In addition to its core functionalities, the system includes built-in data privacy and security measures to comply with regulations such as HIPAA. These protocols are essential for protecting patient information during the diagnostic process, particularly in environments where sensitive data is handled. Security features include encrypted data transmission, secure access controls, and routine audits to ensure compliance with healthcare regulations.
[023] The implementation of continuous learning mechanisms within the system allows it to adapt to new data and feedback from healthcare professionals. This feature not only helps refine the model's accuracy over time but also ensures that the system evolves in response to changing clinical practices and emerging research. As more data becomes available, the system's predictive capabilities are expected to improve, further enhancing its role in the diagnostic process.
Table 2 below summarizes the primary hardware and software components of the system and their interconnections:

[024] The described system represents a groundbreaking approach to breast cancer diagnostics, addressing critical gaps in current methodologies through the innovative application of AI and deep learning. By combining advanced algorithms, cloud computing, and a modular architecture, the system not only enhances early detection capabilities but also improves the overall efficiency of diagnostic workflows in healthcare settings. The scalability of the system, along with its potential for future adaptations, establishes it as a valuable tool in the ongoing effort to combat breast cancer and improve patient care globally.
[025] Ultimately, the AI-based system for early detection of breast cancer holds the promise of revolutionizing diagnostic practices, providing a non-invasive, accurate, and efficient means of identifying malignancies in their earliest stages. This invention not only contributes to enhanced survival rates but also positions itself as an essential asset in the arsenal of modern oncological care.
[026] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[027] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
, Claims:1. An AI-based system for early detection of breast cancer, comprising:
a) a deep learning algorithm trained on extensive datasets of histopathology images to identify cancerous tissues;
b) a high-performance GPU server configured for real-time image processing;
c) a cloud-based architecture for storing and analyzing histopathology images, enabling rapid access to diagnostic results;
d) an integrated user interface that allows healthcare professionals to upload histopathology images and access diagnostic reports.
2. The system as claimed in claim 1, wherein the deep learning algorithm employs transfer learning from pre-trained models to enhance diagnostic accuracy in identifying cancerous cells.
3. The system as claimed in claim 1, wherein the architecture is adaptable to various imaging formats, allowing seamless integration with existing imaging equipment used in medical facilities.
4. The system as claimed in claim 1, further includes a data security protocol that ensures compliance with healthcare regulations, including HIPAA, to protect patient data during transmission and storage.
5. The system as claimed in claim 1, wherein the AI-based analysis generates comprehensive diagnostic reports highlighting areas of concern and providing probabilistic confidence scores for each detected anomaly.
6. The system as claimed in claim 1, wherein the image processing capabilities are optimized to reduce diagnostic errors, minimizing both false positives and false negatives in breast cancer detection.
7. The system as claimed in claim 1, wherein the cloud-based architecture allows for continuous learning and adaptation of the deep learning algorithm based on new data inputs and user feedback.
8. The system as claimed in claim 1, further including a recommendation engine that suggests follow-up actions based on the findings in the diagnostic report, facilitating timely clinical interventions.
9. The system as claimed in claim 1, wherein the user interface is designed to integrate with existing diagnostic workflows, including Picture Archiving and Communication Systems (PACS), to enhance the clinical workflow.
10. The system as claimed in claim 1, wherein the modular architecture enables future integration with additional imaging modalities, such as MRI and ultrasound, to broaden the system's diagnostic capabilities across various cancer types.

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