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COVIDNet-CXR-Shuffle

This is an open-source DL-based classification model that tries to identify patients with COVID-19 viral infection, non-COVID-19 infection, and those with no infection with high accuracy by analyzing their chest x-ray scans. This project is part of COVID-Net open source initiative. This is a prototype model, not intended to be used yet in production. You can reach out to me directly on LinedIn if you have questions about this model or want help to getting it used as an experimental tool for COVID-19 near real-time screening.

<img src="reports/covid-19-explainability-exmaple.png" style="display: block; margin-left: auto; margin-right: auto; width: 100%;">

Key points on COVIDNet-CXR-Shuffle network

  1. The architecture was adapted from ShuffleNet v2
  2. Designed with efficiency in mind to allow near real-time screening with mobile devices
  3. Experimented with transfer learning and data augmentation techniques to improve generalizability and robustness
  4. This project is built using open-source software, where PyTorch was used as the main AI framework
  5. Training and testing datasets relied on COVIDx dataset which consists of chest x-ray images from 3 publicly available data.
  6. For model evaluation, I relied on the images listed in test_COVIDx2.txt as the blind testset used for evaluation.

Project structure

Performance metrics and benchmarks