🧠 SARVANDANI - Brain Tumor Analysis System

A full-stack application for AI-powered brain tumor detection from MRI images using deep learning.

🌐 Live Application

🔗 Backend API: https://full-stack-brain-tumor-analysis.onrender.com
🔗 Frontend: Deployed on Render (Static Site)

🚀 Tech Stack

Frontend

• Single HTML File - No build process, no MIME type issues
• Vanilla JavaScript - No framework dependencies
• Modern CSS - Responsive design

Backend

• FastAPI - Modern Python web framework
• TensorFlow 2.15 - Deep learning framework
• Keras - High-level neural network API
• CNN Architecture - Convolutional Neural Network for image classification

AI/ML

• Deep Learning Model - Trained CNN for tumor detection
• Data Augmentation - Improved model accuracy
• Confidence Scores - Detailed prediction metrics

📋 Features

• 🧠 AI-Powered Analysis - Deep learning model for tumor detection
• ⚡ Fast Processing - Get results in seconds
• 📊 Confidence Scores - Detailed prediction metrics
• 🖼️ Image Upload - Drag and drop or click to upload
• 📱 Responsive Design - Works on all devices

🛠️ Installation

Prerequisites

• Python 3.9 or higher
• pip (Python package manager)

Setup

1. Clone the repository

   git clone https://github.com/Sarvandani/Full_stack_Brain_Tumor_Analysis.git
   cd Full_stack_Brain_Tumor_Analysis

2. Install backend dependencies

   cd backend
   pip install -r requirements.txt

3. Train the model (if not already trained)

   python train_model.py

🚀 Running Locally

Start Backend

cd backend
python main.py

The backend will start on http://localhost:5001

Start Frontend

Simply open frontend/index.html in your browser, or use a local server:

cd frontend
python -m http.server 4001

Then open http://localhost:4001 in your browser.

📦 Project Structure

Full_stack_Brain_Tumor_Analysis/
├── backend/
│   ├── main.py              # FastAPI application
│   ├── train_model.py       # Model training script
│   ├── requirements.txt     # Python dependencies
│   └── models/              # Trained model (generated)
├── frontend/
│   ├── index.html           # Single HTML file (all-in-one)
│   └── images/              # MRI sample images
├── data/
│   └── brain_tumor_dataset/ # Training dataset
├── render.yaml              # Render deployment config
└── README.md               # This file

🌐 Deployment on Render

Backend Deployment

1. Connect GitHub repository to Render
2. Create new Web Service
3. Configure:
   • Build Command: cd backend && pip install -r requirements.txt
   • Start Command: cd backend && python main.py
   • Environment Variables:
     • PORT: 5001
     • ALLOWED_ORIGINS: Your frontend URL

Frontend Deployment

1. Create new Static Site on Render
2. Configure:
   • Build Command: echo "No build needed"
   • Publish Directory: frontend
3. Update backend CORS with frontend URL

Or use render.yaml for automatic deployment.

📝 API Endpoints

• GET /health - Health check
• POST /predict - Analyze brain MRI image for tumor detection
• POST /train - Train the model (background task)
• GET /train/status - Check training status

⚙️ Configuration

• Backend Port: 5001 (configurable via PORT env var)
• Frontend Port: 4001 (local development)
• Model Path: backend/models/brain_tumor_model.keras

⚠️ Important Disclaimer

This tool is for educational and research purposes only. It should not be used as a substitute for professional medical diagnosis, treatment, or advice. Always consult with qualified medical professionals for medical decisions and diagnosis.

👨‍💻 Author

SARVANDANI

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For questions, issues, or contributions, please open an issue on GitHub.

📊 Dataset

The dataset used in this project can be downloaded from:

• Brain MRI Images for Brain Tumor Detection - Kaggle