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chest-xray-diagnosis

v1.0.1

Published

AI-powered chest X-ray diagnostic assistant — auto-installs the Python backend and launches the API server via npx.

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Links

Git

Maintainers

konamarthi-nitish-veera-venkateshkonamarthi-nitish-veera-venkatesh

Keywords

medical-aichest-xraychexnetdiagnosticdeep-learningradiology

Readme

chest-xray-diagnosis (NPM)

An AI-powered medical diagnostic system for chest X-ray analysis, designed to be launched instantly via NPM.

This is a Node.js wrapper around the Python chest-xray-diagnosis package. It gives front-end and Node developers zero-config access to a powerful PyTorch X-ray diagnostic backend.

🚀 Quick Start (Zero Config)

Run the API server instantly:

npx chest-xray-diagnosis serve --port 8000

What happens under the hood?

  1. Detects if you have Python 3.9+ installed.
  2. Automatically creates an isolated virtual environment at ~/.chexray-diagnosis/venv/.
  3. Installs the core Python machine learning libraries (PyTorch, FastAPI, etc.).
  4. Downloads the pre-trained X-ray model weights (~230 MB) from HuggingFace Hub.
  5. Launches the REST API server!

📋 Prerequisites

  • Node.js 16+
  • Python 3.9+ installed on your system
  • (Optional) API Keys:

Note: The core ML analysis (disease detection & heatmaps) works 100% locally without any API keys!

💻 CLI Commands

You can use npx to run any of the underlying CLI commands:

Start the API Server

npx chest-xray-diagnosis serve --port 8000

Configure API Keys (Interactive Wizard)

npx chest-xray-diagnosis setup

Securely saves your API keys so the local server can use Gemini and Geoapify.

Pre-Download Models

npx chest-xray-diagnosis download-models

Manually downloads the PyTorch model weights to your local cache.

Analyze a Single Image from Terminal

npx chest-xray-diagnosis analyze path/to/xray.png

🧠 What it does

When an image is sent to the server, it runs through a powerful 4-stage pipeline:

  1. Binary Pipeline: Validates the image (is it garbage? is it a chest?).
  2. CheXNet (DenseNet-121): Detects 14 different lung pathologies (Pneumonia, Mass, Effusion, etc.).
  3. Grad-CAM++: Generates a visual heatmap (returned as base64) highlighting the diseased areas of the lung.
  4. Gemini AI: Generates a patient-friendly clinical explanation of the findings.

License & Disclaimer

MIT License.

Medical Disclaimer: For research and educational use only. Not a substitute for professional medical diagnosis. Always consult a qualified healthcare provider.