themirachathumina / medai

This application leverages AI to analyze medical images, specifically brain images and chest X-rays. Users can upload medical images, get predictions and descriptions about potential issues, and verify or correct these predictions.

Medical Image Analysis with AI

This application leverages AI to analyze medical images, specifically brain images and chest X-rays. Users can upload medical images, get predictions about potential issues, and verify or correct these predictions. The application ensures a smooth and secure user experience through a robust authentication system.

Features

• User Authentication: Secure login system to protect user data.
• Medical Image Analysis: Upload and analyze brain images and chest X-rays.
• AI Predictions: Get AI-generated predictions for uploaded images.
• User Verification: Verify or correct AI predictions to improve accuracy.
• Responsive Design: User-friendly interface

Login Page

• Secure login page powered by Asgardeo for user authentication.

Home Page

• The home page allows users to select between chest X-ray analysis and brain image analysis.

Select Scan

• After login, users can choose between different types of medical scans to analyze.

Brain Image Layout

• Upload brain images for AI analysis. The AI predicts the presence of tumors, and users can verify or correct these predictions.

Chest X-ray Layout

• Upload chest X-rays for AI analysis. The AI detects issues such as pneumonia and provides a detailed analysis.

• Users can interact with the system to get more detailed explanations of the findings with chat.

Prediction Verification

• Users can correct AI predictions, enhancing the learning and accuracy of the model.

Setup Instructions

To run this application locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ThemiraChathumina/medAI.git
   cd medAI
   npm install
   

2. Update the config.ts in the root directory and add your Asgardeo configuration details::

   const baseURL = "http://localhost:5173";
   
   export const config = {
     signInRedirectURL: baseURL,
     signOutRedirectURL: `${baseURL}/login`,
     clientID: "YOUR_CLIENT_ID",
     baseUrl: "https://api.asgardeo.io/t/YOUR_TENANT_NAME",
     scope: ["openid", "profile"],
   };

3. Setup and run the backend in medAI Backend.

4. Run the application:

   npm run dev
   

Credits

This application uses models and techniques described in the following paper:

Interactive and Explainable Region-guided Radiology Report Generation
Tim Tanida, Philip Müller, Georgios Kaissis, Daniel Rueckert
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

The code for the models can be found in their GitHub repository.

The following datasets were used for model training:

• Brain Tumor Classification (MRI) Dataset
• Chest X-ray Pneumonia Dataset