🫁 AI-Powered Chest CT Scan Diagnostic Platform

A professional PACS-style medical imaging application that uses deep learning to analyze chest CT scans and detect potential malignancies.

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✨ Features

• AI Diagnosis - 4-class classification: Adenocarcinoma, Large Cell, Normal, Squamous Cell
• Professional UI - Dark PACS-style interface with glassmorphism effects
• Real-time Analysis - Upload CT scans and get instant AI-powered insights
• Confidence Visualization - Color-coded confidence bars for all classes
• Modern Design - Collapsible sidebar, floating controls, thumbnail navigation

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🛠️ Tech Stack

Layer	Technology
Frontend	React 19, Vite, Tailwind CSS v4
Backend	FastAPI, Python 3.9+
AI Model	TensorFlow/Keras 3.10, CNN (VGG-based)
Styling	Custom CSS with CSS variables, Glassmorphism

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🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 18+
• npm

1. Clone the Repository

git clone https://github.com/muhammadtihame/Cancer-Detection_Using_CNN.git
cd Cancer-Detection_Using_CNN

2. Start the Backend

cd backend
pip install -r requirements.txt
python -m uvicorn main:app --reload --port 8000

3. Start the Frontend

cd frontend
npm install
npm run dev

4. Open the App

Navigate to http://localhost:5173 in your browser.

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📁 Project Structure

chest-ct-diagnostic/
├── backend/
│   ├── main.py              # FastAPI server
│   └── requirements.txt     # Python dependencies
├── frontend/
│   ├── src/
│   │   ├── App.jsx          # Main React component
│   │   ├── index.css        # PACS-style dark theme
│   │   └── main.jsx         # React entry point
│   ├── index.html           # HTML template
│   └── package.json         # npm dependencies
├── model/
│   └── chest_ct_model.h5    # Pre-trained CNN model
├── labels.txt               # Class labels
└── README.md

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🔌 API Endpoints

Method	Endpoint	Description
GET	/	Health check
GET	/health	Detailed health with model status
POST	/predict	Analyze CT scan image

Example Request

curl -X POST "http://localhost:8000/predict" \
  -F "file=@ct_scan.jpg"

Example Response

{
  "diagnosis": "Adenocarcinoma",
  "confidence": 87.3,
  "all_predictions": {
    "Adenocarcinoma": 87.3,
    "Large Cell": 8.2,
    "Normal": 3.1,
    "Squamous Cell": 1.4
  }
}

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⚠️ Disclaimer

This application is for educational and research purposes only. AI predictions should not replace professional medical diagnosis. Always consult qualified healthcare providers for medical decisions.

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👤 Author

Made by Mohammad Tihame

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📄 License

MIT License - feel free to use and modify for your projects.