The Medical Image Analysis - Abnormality Detection System is a deep learning-based solution that aims to identify and classify abnormalities in medical images such as X-rays and MRIs. The system utilizes object detection techniques to locate and identify abnormal areas in the images, and provides real-time recommendations for diagnosis and treatment.
The main objective of this project is to assist healthcare professionals in accurately and efficiently detecting abnormalities in medical images, which can aid in early diagnosis and treatment planning. The system is designed to be user-friendly and accessible, with a user-friendly interface and easy integration into existing medical imaging workflows.
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Object Detection: The system uses state-of-the-art object detection algorithms to identify and locate abnormalities in medical images. It can detect various types of abnormalities such as tumors, fractures, nodules, and other pathologies.
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Classification: The system is capable of classifying the detected abnormalities into different categories based on their characteristics and features. This can assist in providing more accurate diagnosis and treatment recommendations.
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Real-time Recommendations Once the abnormalities are detected and classified, the system provides real-time recommendations for diagnosis and treatment. These recommendations are based on pre-defined rules and guidelines, which can be customized according to specific requirements.
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User-friendly Interface: The system is designed with a user-friendly interface that allows healthcare professionals to easily upload medical images, view the detected abnormalities, and access the recommendations. The interface is intuitive and easy to navigate, making it accessible to users with varying levels of technical expertise.
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Integration: The system can be easily integrated into existing medical imaging workflows, such as Picture Archiving and Communication Systems (PACS) or Radiology Information Systems (RIS), to streamline the abnormality detection process and enhance the efficiency of healthcare providers.
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Python: The system is implemented in Python, which provides a rich ecosystem of libraries and tools for image processing, machine learning, and deep learning.
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Deep Learning Framework: The system utilizes a deep learning framework TensorFlow, to implement the object detection and classification algorithms.
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Image Processing Libraries: The system uses image processing library PIL, to pre-process the medical images before feeding them into the deep learning models. The library provide functions for image resizing, normalization, and augmentation, which are essential for improving the accuracy and robustness of the models.
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Web Framework: The system employs a web framework, Flask, to build the user-friendly interface and handling HTTP requests and responses.
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MySQL: The system uses MySQL, for storing and managing medical image data, detected abnormalities, and recommendations.
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HTML5, CSS, JavaScript, Bootstrap: The system utilizes HTML5, CSS, JavaScript, and the Bootstrap framework for front-end development.
- Install Python: If you don't have Python installed, download and install the latest version of Python from the official Python website here.
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Install Dependencies: Install the required dependencies, such as TensorFlow, PIL, MySQL connector for Python, and the web framework Flask, using the package manager of your choice (pip or conda).
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Install MySQL: Install MySQL server and client on your machine, and configure the necessary database and tables for storing medical image data, detected abnormalities, and recommendations.
- Clone the Repository: Clone the Medical Image Analysis - Abnormality Detection System repository
git clone https://github.com/Ethiopian-boy/Medical-Image-Analysis.git
cd Medical-Image-Analysis
python3 main.py