Multi-disease-chest-xray-ai

Multi-Disease Chest X-ray AI System

A modular deep learning system that runs multiple independent chest X-ray models
(TB, COVID-19, and NIH multi-disease) in parallel on a single input image.

The system is designed with strict model isolation, unified orchestration, and explainability where clinically appropriate. It is deployable locally and not tied to notebook-based workflows.

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Features

• Independent disease-specific deep learning models
• Parallel inference on a single X-ray input
• Grad-CAM explainability where applicable
• Multi-label prediction support (NIH model)
• Unified Streamlit-based interface
• Local deployment (VS Code ready)

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System Overview

The application orchestrates three independent pipelines:

• Tuberculosis Detection (ResNet-based, single-label)
• COVID-19 Detection (DenseNet-based, single-label with risk mapping)
• NIH Multi-Disease Detection (DenseNet-based, multi-label sigmoid output)

Each model is trained, loaded, and executed independently to avoid label interference and to preserve interpretability.

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How to Run

• pip install -r requirements.txt
• streamlit run app.py

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Contributors