Bone Fracture Detection

Bone Fracture Detection is a web-based application that utilizes machine learning to identify fractures in X-ray images. This application helps healthcare professionals analyze X-ray scans efficiently and accurately. The frontend is built with Tailwind CSS for a modern and responsive UI, while the backend consists of Flask for image processing and Spring Boot for management and user data handling.

Features

• Fracture Detection: Uses a machine learning model to detect fractures in uploaded X-ray images.
• User Interface: A clean, modern interface built with Tailwind CSS for responsiveness and ease of use.
• Image Upload: Users can upload X-ray images for analysis.
• Real-time Results: Provides immediate feedback on whether the X-ray shows a fracture or not.
• Management System: Spring Boot-based system for managing user data, scan history, and reports.
• Role-Based Access: Doctors and patients have different access privileges.
• API Communication: Flask handles image processing, and Spring Boot manages user-related functionalities.

Tech Stack

• Frontend: HTML, CSS (Tailwind CSS), JavaScript
• Backend: Flask (Python) for image processing and machine learning
• Management System: Spring Boot (Java) for user data and scan history management
• Machine Learning: TensorFlow, Keras for deep learning-based image analysis
• Image Processing: OpenCV or similar libraries for preprocessing X-ray images

Installation and Usage

Prerequisites

Ensure you have the following installed:

• Python 3.9+
• Java 17+
• Flask and required Python libraries (pip install flask tensorflow keras opencv-python)
• Spring Boot dependencies (Spring Web, Spring Data JPA, Spring Security)
• Tailwind CSS for the frontend

Contribution

Feel free to contribute by improving the machine learning model, enhancing the UI, or optimizing backend performance. Fork the repo and submit a pull request!

License

This project is open-source and available under the MIT License.