🎯 Cancer Image Classification with AutoML-Driven Neural Architecture Optimization

A cutting-edge medical AI system that automatically designs and trains highly accurate convolutional neural networks for lung and colon cancer detection using Neural Architecture Search (NAS) and Explainable AI.

📖 Table of Contents

• Overview
• Key Features
• Architecture
• Results
• Quick Start
• Project Structure
• Usage
• Model Details
• API Documentation
• Contributing
• License

🌟 Overview

This project demonstrates a modern approach to medical AI by leveraging Automated Machine Learning (AutoML) and Neural Architecture Search (NAS) to automatically design optimal neural network architectures for cancer image classification. The system achieves 99.04% validation accuracy on the LC25000 dataset, significantly outperforming traditional manually-designed models.

🎯 Key Achievements

• 99.04% validation accuracy - State-of-the-art performance
• Automated architecture design - No manual neural network engineering required
• Explainable AI - Grad-CAM visualizations and textual explanations
• Production-ready web interface - Professional React.js frontend with Flask backend

✨ Key Features

🔬 Advanced AI Capabilities

• Neural Architecture Search (NAS): Automatically explores thousands of possible CNN architectures
• Explainable AI (XAI): Grad-CAM heatmaps showing decision regions
• Real-time Predictions: Fast inference with professional web interface
• Multi-class Classification: Detects 5 types of lung and colon tissues

💻 Technical Excellence

• AutoML Pipeline: End-to-end automated model design and training
• Hyperparameter Optimization: Intelligent search space exploration
• Data Augmentation: Comprehensive image preprocessing pipeline
• Model Interpretability: Detailed explanations for medical professionals

🏗️ Architecture

System Overview

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   React.js      │    │   Flask API      │    │   PyTorch       │
│   Frontend      │◄──►│   Backend        │◄──►│   NAS Model     │
│                 │    │                  │    │                 │
│ - Model Testing │    │ - Image Preproc  │    │ - 99.04% Acc    │
│ - Explanations  │    │ - Predictions    │    │ - Grad-CAM      │
│ - Results Viz   │    │ - Explanations   │    │ - AutoML        │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Neural Architecture Search Pipeline

1. Search Space Definition: 15+ architectural parameters
2. Configuration Sampling: Intelligent random sampling
3. Rapid Evaluation: Fast training with early stopping
4. Best Model Selection: Performance-based architecture selection
5. Final Training: Full training of optimal architecture

📊 Results

Performance Metrics

Metric	NAS-Optimized Model	Traditional CNN	Improvement
Validation Accuracy	99.04%	89.6%	+9.44%
Validation Loss	0.015	0.24	+93.75%
Training Time	5 hours (NAS + Training)	1 hour	+4 hours
Generalization	Excellent	Good	Better

Class Detection Performance

The model accurately detects five tissue types:

• Lung Benign (Non-cancerous lung tissue)
• Lung Adenocarcinoma (Lung cancer type)
• Lung Squamous Cell Carcinoma (Lung cancer type)
• Colon Adenocarcinoma (Colon cancer type)
• Colon Benign (Non-cancerous colon tissue)

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• PyTorch 2.0+
• CUDA (optional, for GPU acceleration)

Installation

1. Clone the Repository

git clone https://github.com/Villwin007/Cancer-Image-Classification-with-AutoML-Driven-Neural-Architecture-Optimization.git
cd Cancer-Image-Classification-with-AutoML-Driven-Neural-Architecture-Optimization

2. Backend Setup

cd backend
pip install -r requirements.txt
python app.py

3. Frontend Setup (New Terminal)

cd lung-cancer-detection-web
npm install
npm run dev

4. Access the Application

• Frontend: http://localhost:5173
• Backend API: http://localhost:5000

Quick Demo

1. Open http://localhost:5173 in your browser
2. Navigate to "Model Testing" tab
3. Upload a lung/colon tissue image
4. View AI predictions with explanations

📁 Project Structure

Cancer-Image-Classification/
├── Model_Training
├── backend/
│   ├── app.py                 # Flask API with Grad-CAM explanations
│   ├── requirements.txt       # Python dependencies
│   └── lung_colon_cancer_nas_model_*.pth  # Trained NAS model
│   ├── src/
│   │   ├── components/        # React components
│   │   │   ├── Header/        # Application header
│   │   │   ├── Tabs/          # Tabbed interface
│   │   │   ├── InfoTab/       # Project information
│   │   │   ├── TestTab/       # Model testing interface
│   │   │   ├── UploadInterface/ # Image upload component
│   │   │   ├── Results/       # Prediction results
│   │   │   ├── ExplanationView/ # AI explanations
│   │   │   └── Footer/        # Project information
│   │   ├── App.jsx            # Main application component
│   │   └── main.jsx           # Application entry point
│   ├── package.json           # Node.js dependencies
│   └── vite.config.js         # Vite configuration
└── README.md                  # This file

💻 Usage

Model Training

# Run Neural Architecture Search
python training_code.py

# This will:
# 1. Perform NAS with 15 configurations
# 2. Select best architecture (99.04% accuracy)
# 3. Train final model
# 4. Generate performance plots and reports

Web Interface Usage

1. Project Information Tab: Learn about NAS and model architecture
2. Model Testing Tab: Upload images for AI analysis
3. Results View: See predictions with confidence scores
4. Explanation Panel: Understand AI reasoning with heatmaps

API Usage

# Health check
curl http://localhost:5000/health

# Model information
curl http://localhost:5000/model-info

# Prediction with explanation
curl -X POST -F "image=@path_to_image.jpg" http://localhost:5000/predict-with-explanation

🔧 Model Details

Neural Architecture Search Configuration

SEARCH_SPACE = {
    'num_conv_blocks': [3, 4, 5],
    'filters_multiplier': [1, 2, 4],
    'kernel_sizes_options': [[3,3,3], [3,5,3], [5,3,5], [3,3,5,3], [3,5,5,3]],
    'use_batch_norm': [True, False],
    'conv_dropout': [0.1, 0.2, 0.3],
    'dense_units_options': [[512,256], [256,128], [512], [256]],
    'dense_dropout': [0.3, 0.4, 0.5],
    'learning_rate': [1e-3, 1e-4, 1e-5]
}

Best Model Architecture

• Convolutional Blocks: 4 layers with 4× filter multiplier
• Kernel Sizes: [3, 3, 5, 3] optimized pattern
• Dense Layers: [256, 128] units with 0.5 dropout
• Learning Rate: 0.0001 with ReduceLROnPlateau scheduler
• Batch Size: 32 with extensive data augmentation

🌐 API Documentation

Endpoints

GET /health

Check API status and model loading status.

Response:

{
  "status": "healthy",
  "model_loaded": true,
  "mode": "real",
  "explainable_ai": true
}

GET /model-info

Get detailed model information and configuration.

Response:

{
  "model_name": "NAS-Optimized Cancer Detection Model",
  "validation_accuracy": "99.04%",
  "model_loaded": true,
  "classes": ["Colon Adenocarcinoma", "Colon Benign", ...],
  "features": ["Grad-CAM explanations", "Textual reasoning"]
}

POST /predict-with-explanation

Analyze image with AI explanations.

Request:

curl -X POST -F "image=@tissue_image.jpg" http://localhost:5000/predict-with-explanation

Response:

{
  "success": true,
  "predictions": [...],
  "confidence": 0.9904,
  "diagnosis": "Lung Adenocarcinoma",
  "explanation": {
    "textual": "The model detected irregular glandular patterns...",
    "heatmap_image": "base64_encoded_image",
    "key_factors": ["Glandular architecture", "Nuclear features"]
  }
}

🤝 Contributing

We welcome contributions to enhance this medical AI system! Here's how you can help:

Areas for Improvement

• Model Performance: Experiment with different NAS algorithms
• Explainability: Enhance Grad-CAM and add LIME/SHAP explanations
• Frontend Features: Add prediction history and comparison tools
• Deployment: Docker containers and cloud deployment scripts

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a Pull Request

Reporting Issues

Please use the GitHub issue tracker for:

• Bug reports
• Feature requests
• Documentation improvements
• Security vulnerabilities

📜 License

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

Citation

If you use this work in your research, please cite:

@software{CancerClassificationNAS2025,
  author = {Dhanush Saravanan},
  title = {Cancer Image Classification with AutoML-Driven Neural Architecture Optimization},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Villwin007/Cancer-Image-Classification-with-AutoML-Driven-Neural-Architecture-Optimization}}
}

⚠️ Important Disclaimer

Medical Disclaimer: This tool is for research and educational purposes only. It should not be used for actual medical diagnosis, treatment, or clinical decision-making. Always consult qualified healthcare professionals for medical advice.

Research Use: The model achieves 99.04% accuracy on the LC25000 dataset but should be clinically validated before any medical application.

📞 Support

For questions, issues, or collaborations:

• GitHub Issues: Create an issue
• Email: s.dhanush1106@gmail.com
• Documentation: See code comments and this README

🎓 Academic Impact

This project demonstrates:

• AutoML superiority over manual architecture design in medical imaging
• Explainable AI importance in healthcare applications
• Neural Architecture Search practical implementation
• End-to-end ML system design from research to deployment

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Advancing medical AI through automated machine learning and explainable artificial intelligence.