🏥 Medical Report Analysis - X-Ray Analysis 🩻

Overview

Medical Report Analysis is a web-based application designed to analyze and interpret X-ray images. By integrating deep learning with modern web technologies, this project features a React-based frontend and a deployed machine learning backend. It leverages a pre-trained model for medical image classification, enhancing diagnostic capabilities with real-time predictions.

The application allows users to upload X-ray images, analyze them using a machine learning model, and view detailed insights and predictions about the medical condition represented in the images.

Features ✨

• X-ray Image Upload: Users can upload X-ray images for analysis via the React interface.
• Deep Learning Model Integration: The backend uses a trained .h5 model to classify medical images, which is accessible through an API.
• Interactive Web Interface: The web application provides a seamless and user-friendly interface, built with React.
• Visualization: After uploading an image, the system displays detailed analysis and insights, providing a clear understanding of the results.
• Secure Storage: Uploaded X-ray images are securely managed and stored through the backend.
• Live Deployment: The project is fully deployed and connected, with both the frontend and backend working live in a production environment.

Technologies Used 🛠️

• Frontend: React, HTML, CSS, JavaScript
• Backend: Python (Flask or similar), TensorFlow/Keras
• Deployment: Vercel (for both frontend and backend)
• Notebook: Jupyter Notebook for model training and testing

Model Details 📊

• The model.h5 file is a deep learning model trained on medical X-ray datasets. It uses convolutional neural networks (CNNs) for feature extraction and classification.
• The model can detect medical conditions from X-ray images and return relevant insights and predictions.
• Full training details and model architecture are available in the X_RAY.ipynb notebook file.
• The model is hosted on the live backend and accessed via API calls for real-time predictions.

Screenshots 📸

Here are some screenshots of the application to give you a visual idea of the interface and results:

Deployment 🌍

• The application is fully deployed and available for use at medicure.ayushsharma.site.
• The React frontend and ML backend are live, connected, and operational.

Acknowledgements 🙏

• Inspiration from advancements in medical image processing.
• Datasets and resources used for training the model.
• Thanks to the community for React and machine learning model development.