🏥 MedVisionAI

Advanced Medical Image Analysis System

AI-Powered Medical Diagnostics for the Modern Healthcare

🚀 Live Demo • 📖 Documentation • 🐛 Report Bug • ✨ Request Feature

🌟 Overview

MedVisionAI revolutionizes medical imaging with AI-powered diagnostic precision. Our system combines state-of-the-art YOLO segmentation models with Google's Gemini AI to provide comprehensive analysis of medical scans including CT, MRI, X-ray, and ultrasound images.

🎯 Key Features

🔬 Multi-Modal Analysis: Support for liver, brain MRI, eye, kidney, and lung imaging
⚡ Real-time Detection: Instant analysis with confidence scoring and visualization
🤖 AI-Powered Insights: Integration with Google Gemini AI for professional interpretations
📄 Automated Reports: PDF reports with findings, confidence scores, and recommendations
🎨 Advanced Visualization: Pseudo-coloring and bounding box visualization
🌐 Web Interface: User-friendly Flask web application
📊 Batch Processing: Support for multiple model analysis on single images

🏥 Supported Medical Conditions

Organ	Conditions Detected	Applications
🫀 Liver	Liver tumors, normal tissue	Hepatocellular carcinoma detection, liver health assessment
🧠 Brain MRI	Brain tumors, anatomical structures	Neurological disorder diagnosis, tumor detection
👁️ Eye	Cataract, Diabetic Retinopathy, Glaucoma, Normal	Ophthalmological screening, preventive care
🫘 Kidney	Kidney stones	Urological assessment, stone detection
🫁 Lung	Lung tumors/cancer	Pulmonary oncology, early cancer detection

🏗️ System Architecture

Workflow Diagram

graph TD
    A[Upload Medical Image] --> B[File Validation]
    B --> C[Model Selection]
    C --> D{Single Model or Multi-Model?}
    D -->|Single| E[YOLO Model Processing]
    D -->|Multi| F[All Models Processing]
    E --> G[Detection & Visualization]
    F --> G
    G --> H[Pseudo-Coloring Application]
    H --> I[Gemini AI Analysis]
    I --> J[Generate PDF Report]
    J --> K[Return Results]

Model Inventory

graph TD
    L[YOLO Models]
    L --> M[Liver Model - liver_tumour.pt]
    L --> N[Brain Model - brain_mri.pt]
    L --> O[Eye Model - eye.pt]
    L --> P[Kidney Model - kidney.pt]
    L --> Q[Lung Model - lung cancer.pt]

Key Components

Image Upload & Validation: Secure file handling with format verification
Multi-Model Detection: Parallel processing across specialized YOLO models
Visualization Engine: Bounding box rendering with pseudo-coloring for anomalies
AI Analysis: Google Gemini integration for medical insights
Report Generation: Automated PDF creation with comprehensive findings

⚙️ Installation

📋 Prerequisites

Python 3.8 or later
pip (Python package installer)
CUDA-compatible GPU (recommended for faster inference)
Git (for cloning the repository)

🚀 Quick Start

# 1. Clone the repository
git clone https://github.com/kknani24/medicure-ai-.git
cd MedVisionAI-main

# 2. Create virtual environment
python -m venv venv

# 3. Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# 4. Install dependencies
pip install -r requirements.txt

# 5. Run the application
python application.py

📦 Manual Installation

Click to expand detailed installation steps

Step 1: Clone the Repository

git clone https://github.com/kknani24/medicure-ai-.git
cd MedVisionAI-main

Step 2: Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Step 3: Install Required Packages

pip install -r requirements.txt

Step 4: Verify Installation

python -c "import application; print('✅ Installation successful!')"

🔧 Configuration

🔑 Environment Setup

Create a .env file in the project root:

# Google Gemini API Key
# Get your API key from: https://makersuite.google.com/app/apikey
GEMINI_API_KEY=your_google_gemini_api_key_here

# Flask Configuration
FLASK_ENV=development
FLASK_DEBUG=True

📁 Model Files

Ensure all YOLO model files are present in the models/ directory:

models/
├── liver_tumour.pt      # Liver tumor detection
├── brain_mri.pt         # Brain MRI analysis
├── eye.pt               # Eye condition detection
├── kidney.pt            # Kidney stone detection
└── lung cancer.pt       # Lung cancer detection

Step 3: Directory Structure

MedVisionAI-main/
├── application.py
├── .env
├── requirements.txt
├── static/
│   └── uploads/
├── templates/
│   ├── index.html
│   └── simulate.html
├── models/
│   ├── liver_tumour.pt
│   ├── brain_mri.pt
│   ├── eye.pt
│   ├── kidney.pt
│   └── lung cancer.pt
└── README.md

🚀 Usage

🖥️ Web Interface

Start the Application
```
python application.py
```
Access the Interface
- Open your browser and navigate to http://localhost:5000
- The application will be running in debug mode
Upload and Analyze
- Select a medical image (PNG, JPG, JPEG formats supported)
- Choose a specific model or leave blank for multi-model analysis
- Click "Analyze Image" to process
- View results, AI insights, and download the PDF report

🎮 Simulation Mode

Access the surgical simulation interface at http://localhost:5000/simulate for advanced medical procedure training.

Command Line Usage

For programmatic access, you can also use the API endpoints directly:

import requests

# Upload and analyze image
files = {'file': open('medical_scan.jpg', 'rb')}
data = {'model': 'liver'}  # Optional: specify model
response = requests.post('http://localhost:5000/detect', files=files, data=data)
result = response.json()

📡 API Endpoints

POST /detect

Analyzes uploaded medical images and returns detection results.

Parameters:

file (required): Medical image file (PNG, JPG, JPEG)
model (optional): Specific model to use ('liver', 'brain_mri', 'eye', 'kidney', 'lung')

Response:

{
    "message": "Detection completed",
    "original_image": "/static/uploads/scan.jpg",
    "processed_image": "/static/uploads/processed_scan.jpg",
    "detections": {
        "liver": [
            {
                "class": "tumor",
                "confidence": 0.87,
                "bbox": [150, 200, 300, 350]
            }
        ]
    },
    "gemini_response": "AI medical analysis...",
    "pdf_report": "/static/uploads/report_scan.pdf"
}

🛠️ Technologies Used

🧠 Core Technologies

Technology	Version	Purpose
Python	3.8+	Backend development
Flask	2.3.3	Web framework
YOLO	8.0.200	Object detection models
PyTorch	2.0.1	Deep learning framework
OpenCV	4.8.1	Image processing
Google Gemini	Latest	AI medical insights
ReportLab	4.0.4	PDF report generation

🔬 AI/ML Stack

YOLO (You Only Look Once): State-of-the-art real-time object detection
PyTorch: Deep learning framework for model inference
Google Gemini AI: Advanced language model for medical analysis
OpenCV: Computer vision and image processing
NumPy: Numerical computing and array operations

Google Gemini AI

Google's Gemini AI provides advanced multimodal understanding capabilities. In our system, it analyzes medical images alongside detection results to generate professional medical insights, treatment recommendations, and diagnostic interpretations with the expertise of a seasoned clinician.

Flask

Flask serves as the web framework powering our medical analysis platform. Its lightweight nature and extensive ecosystem make it perfect for creating responsive web applications that can handle medical image processing workflows efficiently.

OpenCV

OpenCV handles all computer vision operations including image loading, processing, visualization, and pseudo-coloring. Its optimized algorithms ensure fast image manipulation and enhancement, crucial for real-time medical image analysis.

📁 Code Structure

MedVisionAI-main/
├── application.py            # Main Flask application
├── static/
│   └── uploads/              # Uploaded and processed images
├── templates/
│   ├── index.html            # Main web interface template
│   └── simulate.html         # Simulation interface template
├── models/                   # YOLO model files
├── requirements.txt          # Python dependencies
├── .env                      # Environment variables
└── README.md                # Project documentation

Key Functions

allowed_file(): Validates uploaded file formats
process_image(): Main image processing pipeline
process_multiple_model_results(): Handles multi-model analysis
generate_response_with_image(): Interfaces with Gemini AI
create_pdf_report(): Generates comprehensive PDF reports

🌐 Deployment

🚀 Quick Deployment Options

Platform	Free Tier	Custom Domain	SSL	Difficulty
Render	✅	✅	✅	Easy
Railway	✅	✅	✅	Easy
Heroku	❌	✅	✅	Medium
DigitalOcean	❌	✅	✅	Medium

📋 Deployment Checklist

Update .env with production API keys
Test application locally
Ensure all dependencies are in requirements.txt
Configure environment variables on hosting platform
Set up custom domain and SSL
Test deployed application

🔧 Environment Variables for Production

GEMINI_API_KEY=your_production_api_key
FLASK_ENV=production
FLASK_DEBUG=False
PORT=5000

For detailed deployment instructions, see deploy.md.

🤝 Contributing

We welcome contributions to improve MediCure-AI! Here's how you can contribute:

Fork the Repository

git fork https://github.com/kknani24/medicure-ai-.git

Create a Feature Branch

git checkout -b feature/amazing-feature

Make Your Changes

Add new medical models
Improve detection accuracy
Enhance UI/UX
Add new features

Commit Your Changes

git commit -m "Add amazing feature"

Push to the Branch

git push origin feature/amazing-feature

Open a Pull Request

Areas for Contribution

Model Enhancement: Improve existing models or add new medical condition detection
UI/UX Improvements: Enhance the web interface and user experience
Performance Optimization: Optimize inference speed and memory usage
Documentation: Improve documentation and add tutorials
Testing: Add comprehensive test coverage

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🌟 Star this repository if you found it helpful!

Made with ❤️ for the medical community

Disclaimer

⚠️ Important Medical Disclaimer: This AI system is designed to assist healthcare professionals and should not be used as a substitute for professional medical diagnosis or treatment. Always consult with qualified healthcare providers for medical decisions. The system is intended for educational and research purposes and should undergo proper clinical validation before use in medical practice.

For questions, support, or collaboration opportunities, please open an issue or contact the development team.

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
models		models
templates		templates
.gitignore		.gitignore
Procfile		Procfile
README.md		README.md
app.py		app.py
application.py		application.py
brain_mri.pt		brain_mri.pt
deploy.md		deploy.md
eye.pt		eye.pt
kidney.pt		kidney.pt
liver_tumour.pt		liver_tumour.pt
lung cancer.pt		lung cancer.pt
railway.json		railway.json
render.yaml		render.yaml
requirements.txt		requirements.txt
runtime.txt		runtime.txt
test.py		test.py
test_app.py		test_app.py
test_gemini.py		test_gemini.py
test_index.html		test_index.html

Folders and files

Latest commit

History

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🏥 MedVisionAI

Advanced Medical Image Analysis System

🌟 Overview

🎯 Key Features

📋 Table of Contents

🏥 Supported Medical Conditions

🏗️ System Architecture

Workflow Diagram

Model Inventory

Key Components

⚙️ Installation

📋 Prerequisites

🚀 Quick Start

📦 Manual Installation

Step 1: Clone the Repository

Step 2: Create Virtual Environment

Step 3: Install Required Packages

Step 4: Verify Installation

🔧 Configuration

🔑 Environment Setup

📁 Model Files

Step 3: Directory Structure

🚀 Usage

🖥️ Web Interface

🎮 Simulation Mode

Command Line Usage

📡 API Endpoints

POST /detect

🛠️ Technologies Used

🧠 Core Technologies

🔬 AI/ML Stack

Google Gemini AI

Flask

OpenCV

📁 Code Structure

Key Functions

🌐 Deployment

🚀 Quick Deployment Options

📋 Deployment Checklist

🔧 Environment Variables for Production

🤝 Contributing

Areas for Contribution

📄 License

🌟 Star this repository if you found it helpful!

Disclaimer

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