BRAIN TUMOUR DETECTION USING MACHINE LEARNING

Abstract

The invention relates to a system and method for detecting brain tumour using machine learning techniques applied to magnetic resonance imaging (MRI) scans. The system comprises an image acquisition module to obtain MRI images, a pre-processing module for noise reduction and normalization, and a convolutional neural network (CNN) for feature extraction. The CNN architecture identifies intricate patterns and features indicative of brain tumours, allowing for precise classification and localization. The system provides real-time feedback, including heat maps highlighting tumours regions, aiding radiologists and clinicians in diagnosis and treatment planning. The invention further incorporates a user interface for easy access, visualization, and interpretation of results. The method supports various MRI imaging techniques, such as T1-weighted, T2-weighted, and diffusion-weighted imaging, ensuring comprehensive analysis. The system is designed to integrate

Specification

Description:[0001] The present invention relates to the interdisciplinary fields of medical imaging, machine learning, and artificial intelligence. Specifically, it focuses on the automated detection and diagnosis of brain tumours through the sophisticated analysis of magnetic resonance imaging (MRI) scans. The invention is positioned at the intersection of healthcare and technology, aiming to enhance the accuracy and efficiency of tumour detection, ultimately improving patient care and outcomes.

Background of the Invention
[0002] Brain tumours represent a critical health challenge, being responsible for significant morbidity and mortality worldwide. Traditional methods for diagnosing brain tumours, such as manual interpretation of MRI scans, require extensive expertise and can be time-consuming. These methods may lead to variability in diagnoses based on the radiologist's experience and subjective judgment, which can delay treatment and affect patient prognosis.
[0003] Recent advancements in machine learning and artificial intelligence present opportunities to address these challenges. Machine learning algorithms, particularly deep learning techniques such as convolutional neural networks (CNNs), have demonstrated remarkable success in image classification tasks across various domains, including medical imaging. Previous studies have employed basic machine learning algorithms, but there is a pressing need for more advanced systems that can learn complex patterns and features in MRI images.
[0004] This invention seeks to bridge that gap by utilizing sophisticated image processing algorithms and machine learning techniques, enhancing the speed and accuracy of brain tumours detection. By integrating state-of-the-art technology, the invention aims to provide a reliable tool for radiologists, aiding in rapid decision-making and improving patient management.

Objects of the Invention
[0005] An object of the present invention is to develop a robust automated system capable of accurately detecting and classifying brain tumours from MRI images, reducing the reliance on manual interpretation.
[0006] Another object of the present invention is to utilize advanced machine learning methodologies, particularly convolutional neural networks (CNNs), to enhance feature extraction and pattern recognition in MRI scans.
[0007] Yet another object of the present invention is to implement enhanced image processing algorithms aimed at minimizing false positives, ensuring that non-tumours regions are accurately identified, thereby improving the reliability of diagnoses.
[0008] Another object of the present invention is to provide a user-friendly interface and decision-support tool that assists radiologists in making timely and informed diagnostic decisions based on comprehensive analysis.
[0009] Another object of the present invention is to ensure that the developed system can be seamlessly integrated into existing clinical workflows, addressing practical challenges and adhering to healthcare regulations.
[0010] Another object of the present invention is to establish mechanisms for the continual updating and training of the machine learning model using new data, ensuring the system remains current with the latest advancements in medical imaging and tumours detection.

Summary of the Invention
[0011] According to the present invention, presents a novel approach to brain tumours detection by leveraging machine learning algorithms applied to MRI images. The system employs convolutional neural networks (CNNs) for advanced analysis, allowing for quick and accurate identification and classification of brain tumours.
[0012] The process begins with the acquisition of high-quality MRI images, which undergo extensive pre-processing to enhance their clarity and remove artifacts that may hinder accurate analysis. By utilizing CNNs, the system automatically extracts intricate features from the images, revealing patterns indicative of tumours presence.
[0013] Additionally, the model is trained on a diverse dataset of labeled MRI scans, ensuring its capability to generalize across different patient populations and imaging techniques. The invention also incorporates advanced feature extraction methods that improve the model's accuracy in differentiating between tumours and non-tumours regions, significantly reducing the likelihood of false positives.
[0014] This automated system serves as a valuable tool for radiologists, providing them with insights into tumours localization and classification. Ultimately, the invention aims to facilitate faster diagnostic processes, enabling timely interventions and improving patient outcomes.
[0015] 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.
[0016] 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.


Detailed description of the Invention
[0017] An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
[0018] The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and 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 item.

[0019] The present invention describes a comprehensive system for detecting brain tumours through the advanced analysis of magnetic resonance imaging (MRI) scans, utilizing machine learning and deep learning techniques. The architecture of the system consists of several interconnected components, each playing a crucial role in achieving accurate tumours detection and classification. The following subsections provide an in-depth overview of each component:



1. Image Acquisition:
[0020] MRI Techniques: The initial step involves the acquisition of MRI images, which can be obtained using various scanning techniques. These techniques include:
[0021] T1-weighted Imaging: Useful for visualizing the anatomy of the brain and identifying tumours with distinct borders.
[0022] T2-weighted Imaging: Effective in highlighting edema and pathological changes associated with tumours.
[0023] Diffusion-weighted Imaging (DWI): Provides insights into cellular structure, aiding in the identification of highly cellular tumours.
[0024] Standardization: It is essential that the images be acquired in a standardized format, such as DICOM (Digital Imaging and Communications in Medicine), to ensure uniformity and facilitate subsequent processing. The images are stored in a secure database, allowing for easy access and retrieval.
2. Pre-Processing:
[0025] Normalization: This process adjusts the intensity values of the MRI images to a common scale, ensuring consistency across different scans. It minimizes variability due to differences in machine settings or patient conditions.
[0026] Noise Reduction: Techniques such as Gaussian filtering or bilateral filtering are employed to remove noise and artifacts from the images, which can obscure critical details. This step enhances the overall quality of the images, making it easier for the machine learning model to extract relevant features.
[0027] Histogram Equalization: This technique is applied to improve the contrast of the images, making tumours features more visible. By redistributing pixel intensities, histogram equalization enhances the delineation of the tumours from surrounding healthy tissue.
3. Feature Extraction:
[0028] Convolutional Neural Networks (CNNs): The backbone of the invention is the use of CNNs for feature extraction. CNNs are designed to automatically learn and extract intricate patterns from images through their hierarchical structure:
[0029] Convolutional Layers: The first few layers of the CNN apply multiple filters to the input images, detecting basic features such as edges, corners, and textures. Each filter is learned during the training phase and is optimized to capture features relevant for tumours detection.
[0030] Activation Functions: Non-linear activation functions, such as ReLU (Rectified Linear Unit), are applied after the convolutional operations to introduce non-linearity into the model. This allows the CNN to learn complex relationships within the data.
[0031] Pooling Layers: Max pooling or average pooling layers follow the convolutional layers, reducing the spatial dimensions of the feature maps while retaining the most critical features. Pooling helps to make the model more robust to variations in input images.
[0032] Fully Connected Layers: After several rounds of convolution and pooling, the high-level features are flattened and fed into fully connected layers, which ultimately perform the classification task. The last layer uses a softmax activation function to output probabilities for each class (tumours-present or tumours-absent).
4. Model Training:
[0033] Dataset Preparation: The model is trained on a large dataset of labeled MRI images, ensuring that each image is associated with a known diagnosis. The dataset includes a diverse range of cases, encompassing different tumours types, sizes, and locations to enhance the model's robustness.
[0034] Data Augmentation: To prevent overfitting and increase the diversity of the training data, data augmentation techniques are employed. These include rotations, translations, zooming, and flipping of the images, allowing the model to learn invariant features.
[0035] Training Process: The training involves optimizing the model parameters using a loss function that measures the difference between predicted and actual labels. Backpropagation is used to update the weights, and techniques such as dropout and early stopping are implemented to enhance generalization.
5.Classification and Localization:
[0036] Tumours Classification: Once trained, the model can analyze new MRI images, classifying them as tumours-present or tumours-absent based on the learned features. The classification process is rapid, providing real-time feedback for clinical decision-making.
[0037] Tumours Localization: In addition to classification, the model is capable of localizing the tumours within the brain. This is achieved through techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping), which generates heat maps to highlight areas of interest. These visualizations help radiologists understand the model's predictions and provide insights into tumours morphology.
6. Performance Evaluation:
[0038] Metrics for Evaluation: The performance of the developed system is rigorously evaluated using various metrics, including:
[0039] Accuracy: The proportion of correctly classified images compared to the total number of images.
[0040] Sensitivity (True Positive Rate): The ability of the model to correctly identify positive cases (tumours).
[0041] Specificity (True Negative Rate): The ability of the model to correctly identify negative cases (non-tumours).
[0042] Precision (Positive Predictive Value): The proportion of true positive results among all positive predictions made by the model.
[0043] F1-Score: The harmonic means of precision and recall, providing a balanced measure of the model's performance.
[0044] Cross-Validation: Techniques such as k-fold cross-validation are used to ensure that the model's performance is consistent across different subsets of the dataset, validating its generalizability.
7.Clinical Implementation:
[0045] User Interface: The system features a user-friendly interface designed for radiologists and clinicians, allowing them to easily upload MRI images, initiate analyses, and visualize results. The interface may also include options for manual input of patient data and additional clinical information.
[0046] Integration with Clinical Workflows: Considerations for integrating the system into existing clinical workflows are paramount. The invention addresses potential barriers to adoption, such as ensuring compatibility with hospital information systems (HIS) and electronic health records (EHR), as well as providing training for healthcare professionals.
[0047] Regulatory Compliance: The system is developed in accordance with healthcare regulations and standards, including data privacy laws (e.g., HIPAA in the U.S.) and medical device regulations, ensuring that patient data is handled securely and ethically.
8. Future Directions:
[0048] Expanding Applications: Future research directions include expanding the model to detect other types of brain abnormalities, such as metastases or non-tumours lesions, thereby broadening its clinical utility.
[0049] Multimodal Data Integration: The invention envisions the integration of multimodal data (e.g., genomic data, clinical history) to enhance diagnostic accuracy and facilitate personalized treatment strategies.
[0050] Collaborative Studies: Ongoing collaboration with medical professionals is emphasized to refine the model's predictions continually and adapt to new insights in neuro-oncology.
[0051] Longitudinal Studies: Implementing longitudinal studies to assess the model's effectiveness in tracking tumours progression or response to treatment over time is a potential avenue for future development.
[0052] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.

, Claims:1. A system for detecting brain tumours in magnetic resonance imaging (MRI) scans, comprising:
an image acquisition module configured to acquire MRI images of a patient's brain;
a pre-processing module configured to normalize, filter, and enhance the acquired MRI images to improve image quality;
a feature extraction module employing a convolutional neural network (CNN) architecture to automatically learn and extract features indicative of brain tumours from the pre-processed images; and
a classification module configured to classify the images as tumours-present or tumours-absent based on the extracted features.

2. A method for detecting brain tumours in magnetic resonance imaging (MRI) scans, comprising the steps of:
a) acquiring MRI images of a patient's brain;
b) processing the acquired images to normalize and enhance image quality through noise reduction and histogram equalization;
c) utilizing a convolutional neural network (CNN) to automatically extract features from the processed images;
d) classifying the extracted features to determine the presence or absence of brain tumours;
e) generating heat maps to localize detected tumours within the MRI images;
f) presenting the classification results and heat maps through a user interface for review by medical professionals.

3. The system as claimed in claim 1, wherein the image acquisition module is further configured to acquire MRI images using T1-weighted, T2-weighted, and diffusion-weighted imaging techniques.

4. The system as claimed in claim 1, wherein the pre-processing module applies data augmentation techniques to increase the diversity of the training dataset.

5. The system as claimed in claim 1, wherein the classification module employs a softmax activation function to output probabilities for each class of classification.

6. The system as claimed in claim 1, wherein the user interface further includes functionality for manual input of patient data and clinical history.

7. The system as claimed in claim 1, further comprising a performance evaluation module configured to assess the accuracy, sensitivity, specificity, and precision of the classification results.

8. The method as claimed in claim 2, further comprising the step of acquiring MRI images using multiple imaging techniques, including T1-weighted, T2-weighted, and diffusion-weighted imaging.

9. The method as claimed in claim 2, wherein the processing step includes applying noise reduction techniques such as Gaussian filtering or bilateral filtering to enhance image quality.

10. The method as claimed in claim 2, further comprising the step of utilizing Grad-CAM to generate heat maps indicating tumours localization based on the classification results.

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