ibrahim313/unet-adam-diceloss

🏥 AI-Powered Polyp Detection System

An intelligent medical imaging system that uses deep learning to detect colorectal polyps in colonoscopy images.

🎯 Features

• —Real-time polyp detection using U-Net deep learning architecture
• —Visual segmentation with overlay highlighting detected regions
• —Quantitative analysis providing polyp coverage percentages
• —Medical-grade interface designed for healthcare applications
• —Adjustable sensitivity with detection threshold controls

🔬 Model Details

• —Model Repository: ibrahim313/unet-adam-diceloss
• —Architecture: U-Net with 32 base channels
• —Training Dataset: Kvasir-SEG (1000 polyp images)
• —Framework: PyTorch
• —Input Size: 384×384 pixels
• —Output: Binary segmentation mask

📊 Performance

The model achieves excellent performance on the Kvasir-SEG dataset:

• —High sensitivity for polyp detection
• —Clinically relevant segmentation accuracy
• —Robust performance across various image qualities

🚀 Usage

1. 1.Upload a colonoscopy image
2. 2.Adjust detection threshold if needed (0.1 - 0.9)
3. 3.Click "🔍 Analyze for Polyps"
4. 4.Review the results and segmentation overlay

🔧 Technical Implementation

• —Deep Learning: U-Net encoder-decoder architecture
• —Preprocessing: Albumentations (resize, normalize)
• —Inference: PyTorch with CPU optimization
• —Interface: Gradio for user-friendly interaction
• —Deployment: Hugging Face Spaces

⚠️ Medical Disclaimer

This AI system is intended for research and educational purposes only. It should not be used as a substitute for professional medical diagnosis. Always consult qualified healthcare professionals for clinical decisions.

📝 Model Information

The underlying model was trained using:

• —Loss Function: Dice Loss
• —Optimizer: Adam
• —Training Epochs: 100
• —Validation Strategy: Train/Validation/Test split

🤝 Contributing

This project is open for improvements and contributions. Feel free to:

• —Report issues or bugs
• —Suggest enhancements
• —Share feedback on medical accuracy
• —Contribute to model improvements

📞 Contact

For questions or medical AI collaboration opportunities, please reach out through Hugging Face.

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