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| title: Multi-Class Chest X-Ray Detection | |
| emoji: 🫁 | |
| colorFrom: purple | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.49.1 | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| # 🫁 Multi-Class Chest X-Ray Detection with AST | |
| **AI-powered detection of 4 respiratory diseases from chest X-rays** | |
| ## 🌟 Features | |
| - ✅ **4 Disease Classes**: Normal, Tuberculosis, Pneumonia, COVID-19 | |
| - ✅ **87.29% Validation Accuracy** | |
| - ✅ **100% Pneumonia Specificity** (no TB confusion!) | |
| - ✅ **90% Energy Savings** with Adaptive Sparse Training | |
| - ✅ **Fast Inference**: <2 seconds per X-ray | |
| - ✅ **Explainable AI**: Clear probability distributions | |
| ## 🎯 Key Achievement | |
| **Problem Solved:** Previous binary models misclassified pneumonia as TB (30% false positive rate). | |
| **Our Solution:** Multi-class training distinguishes between all 4 diseases with <5% false positive rate. | |
| | Disease | Test Accuracy | Notes | | |
| |---------|--------------|-------| | |
| | Normal | 60% | Some COVID confusion | | |
| | TB | 80% | Strong performance | | |
| | **Pneumonia** | **100%** | **Perfect - no TB confusion!** | | |
| | COVID-19 | 80% | Good detection | | |
| ## 🔬 Technology | |
| - **Model**: EfficientNet-B2 | |
| - **Training**: Adaptive Sparse Training (AST) | |
| - **Dataset**: COVID-QU-Ex (~33,920 chest X-rays) | |
| - **Sparsity**: 90% (only 10% neurons active) | |
| - **Energy Savings**: 90% vs traditional training | |
| ## ⚠️ Important Medical Disclaimer | |
| **This is a screening tool for research purposes only, NOT a diagnostic device.** | |
| ### Limitations: | |
| - ❌ NOT FDA-approved for clinical diagnosis | |
| - ❌ Cannot replace professional radiologist review | |
| - ❌ All positive results require laboratory confirmation: | |
| - **TB**: Sputum AFB smear, GeneXpert MTB/RIF | |
| - **Pneumonia**: Sputum culture, blood tests | |
| - **COVID-19**: RT-PCR, rapid antigen test | |
| ### Proper Use: | |
| - ✅ Preliminary screening only | |
| - ✅ Always consult healthcare professionals | |
| - ✅ Confirm with clinical correlation and lab tests | |
| **Do not make medical decisions based solely on this tool.** | |
| ## 📊 Performance Metrics | |
| | Metric | Value | | |
| |--------|-------| | |
| | **Overall Accuracy** | 87.29% | | |
| | **Energy Savings** | 90% | | |
| | **Activation Rate** | 10% | | |
| | **Training Epochs** | 50 | | |
| | **Inference Time** | <2 seconds | | |
| ## 🚀 How It Works | |
| 1. **Upload** a chest X-ray image (PNG, JPG) | |
| 2. **Analyze** - AI processes in <2 seconds | |
| 3. **Review** probability distribution for all 4 diseases | |
| 4. **Confirm** with professional medical evaluation | |
| ## 📈 Model Evolution | |
| - **v1.0 (Beta)**: Initial model with EfficientNet-B0 - 87.29% accuracy | |
| - **v2.0 (Current)**: Improved model with EfficientNet-B2 targeting 92-95% accuracy | |
| ## 🔗 Links | |
| - **GitHub**: [oluwafemidiakhoa/Tuberculosis](https://github.com/oluwafemidiakhoa/Tuberculosis) | |
| - **Training Notebook**: [TB_MultiClass_Complete_Fixed.ipynb](https://github.com/oluwafemidiakhoa/Tuberculosis/blob/main/TB_MultiClass_Complete_Fixed.ipynb) | |
| - **Documentation**: [Full README](https://github.com/oluwafemidiakhoa/Tuberculosis/blob/main/README.md) | |
| ## 👨💻 Developer | |
| **Oluwafemi Idiakhoa** | |
| - GitHub: [@oluwafemidiakhoa](https://github.com/oluwafemidiakhoa) | |
| - Hugging Face: [@mgbam](https://huggingface.co/mgbam) | |
| ## 📄 License | |
| MIT License - Free for research and educational use | |
| --- | |
| **Powered by Adaptive Sparse Training - Energy-efficient AI for accessible healthcare** 🌍 |