The full system will include a trained neural network, developed by DarwinAI and the University of Waterloo, containerized in a ChRIS module, developed by Boston Children's Hospital with support from the Massachusetts Open Cloud, Boston University, and many others.

The solution will provide rapid and reliable screening of lung X-rays and CT images, computing the COVID-19 and pneumonia likelihoods as well as highlighting susceptible regions on the images. The system includes data collection, feature extraction, image classification, reporting and visualiztion in a web browser and can be rapidly deployed via a single command line install.

Project Resources

COVID-Net Repo

Repository for AI model

COVID-Net UI

COVID-Net UI

Main UI for submitting and viewing results

ChRIS backend

ChRIS Backend

Core backend service

COVID-Net plugin

COVID-Net plugin

Plugin to ChRIS


If you find a resource that would be useful to link from this page, please send it to .

Video detailing the development of the AI model and project.

Red Hat Team

User Research & Documentation

  • Lisa Knight ‐
  • Jonathan Gershater ‐
  • Roman Luks ‐
  • Astrid Sharpe ‐

Frontend Engineering

  • Adam Jolicoeur ‐
  • Abdullah Sikder ‐
  • Dan Caryll ‐
  • Anuj Singla ‐
  • George Doykan ‐

Container Engineering

  • Jatin Ahuja ‐
  • Willian Rampazzo ‐

Backend Engineering

  • Brad Scalio ‐
  • Aravindh Puthiyaparambil ‐
  • Max Murakami ‐
  • Pratik Jagrut ‐
  • Vedant Nevetia ‐
  • Jun Aruga ‐

QA & Testing

  • Rob Blake ‐

Questions about this project or if you would like to join the team?
Contact Marty Wesley at