README.md

metadata

title: TB Detection with AST
emoji: 🫁
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: true
license: mit
tags:
  - tuberculosis
  - medical-ai
  - chest-xray
  - adaptive-sparse-training
  - explainable-ai
  - gradcam
  - healthcare
  - energy-efficient

🫁 Tuberculosis Detection with Adaptive Sparse Training

Advanced AI for TB screening from chest X-rays - 99.3% accuracy with 89% energy savings!

🌟 Features

• ⚡ Real-time TB Detection from chest X-rays
• 🔬 Grad-CAM Visualization - See what the AI focuses on
• 📊 Confidence Scores with clinical interpretation
• 🎨 Modern UI/UX - Mobile-responsive design
• 💚 Energy Efficient - Uses only 10% of traditional computational resources
• 🌍 Built for Global Health - Runs on low-power devices

🎯 Model Performance

Metric	Value
Accuracy	99.29%
Energy Savings	89.52%
Activation Rate	9.38%
Inference Time	<2 seconds

🚀 How to Use

1. Upload a chest X-ray image (PNG, JPG, JPEG)
2. Enable Grad-CAM to see AI explanations (recommended)
3. Click "Analyze X-Ray" to get results
4. Review prediction, confidence, and clinical interpretation
5. Examine Grad-CAM heatmaps to understand the AI's decision

⚠️ Medical Disclaimer

This is an AI screening tool designed to assist healthcare providers. It is NOT a substitute for professional medical diagnosis, laboratory confirmation, or clinical evaluation by qualified healthcare providers.

Always consult with healthcare professionals for proper diagnosis and treatment.

🔬 Technology

• Architecture: EfficientNet-B0
• Training Method: Adaptive Sparse Training (AST) with Sundew algorithm
• Dataset: TB Chest X-Ray Database (~3,500 images)
• Framework: PyTorch + Gradio

📚 Learn More

• GitHub Repository
• Malaria Detection (Sister Project)

👨‍💻 Developer

Oluwafemi Idiakhoa

• GitHub: @oluwafemidiakhoa
• Hugging Face: @mgbam

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