README.md

---
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

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