Brain MRI Analysis System

A Flask-based web application that analyzes brain MRI images for tumor classification and segmentation.

Features

• MRI Image Upload: Upload brain MRI images for analysis
• Tumor Classification: Identifies the type of tumor (glioma, meningioma, pituitary) or confirms no tumor
• Tumor Segmentation: Visualizes the tumor area with an overlay if present
• Medical Summary: Provides a brief summary of the findings
• Analysis History: Stores all analyses for future reference

Technical Stack

• Backend: Flask (Python)
• Frontend: HTML, Tailwind CSS
• Database: SQLite
• Machine Learning: TensorFlow/Keras
• Models:
  • Brain MRI classification model (brain_mri.h5)
  • U-Net segmentation model (Unet_model.h5)

Setup Instructions

1. Clone the repository

2. Install dependencies

   pip install -r requirements.txt
   

3. Download the pre-trained models

   • Place the models in the root directory:
     • brain_mri.h5 (classification model)
     • Unet_model.h5 (segmentation model)

4. Initialize the database

   • The database will be automatically created when you run the application for the first time

5. Run the application

   export OPENROUTER_API_KEY=your_openrouter_api_key
   python app.py
   

6. Access the application

   • Open a web browser and go to http://127.0.0.1:5000/

Project Structure

├── app.py                  # Main Flask application file
├── brain_mri.db            # SQLite database (created automatically)
├── brain_mri.h5            # Classification model
├── Unet_model.h5           # Segmentation model
├── requirements.txt        # Dependencies
├── static/                 # Static files
│   ├── uploads/            # Uploaded MRI images
│   └── results/            # Generated results
└── templates/              # HTML templates
    ├── base.html           # Base template
    ├── index.html          # Homepage
    ├── result.html         # Results page
    └── history.html        # Analysis history page

Notes

• This application is for educational purposes only and should not be used for actual medical diagnosis.
• The result summary uses OpenRouter through the OpenAI-compatible Python client and expects OPENROUTER_API_KEY to be set.