Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures

Luis Lara, Lucia Eve Berger, Rajesh Raju
2/23/2025
eess.IVcs.CV

No official code found. Best implementation: stwhitfield/covid-severity (1 star, last commit 2025-02-27).

Abstract

The COVID-19 pandemic strained healthcare resources and prompted discussion about how machine learning can alleviate physician burdens and contribute to diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few studies predict the severity of a patient's condition from CXRs. In this study, we produce a large COVID severity dataset by merging three sources and investigate the efficacy of transfer learning using ImageNet- and CXR-pretrained models and vision transformers (ViTs) in both severity regression and classification tasks. A pretrained DenseNet161 model performed the best on the three class severity prediction problem, reaching 80% accuracy overall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases, respectively. The ViT had the best regression results, with a mean absolute error of 0.5676 compared to radiologist-predicted severity scores. The project's source code is publicly available.

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Code Implementations(1)

stwhitfield/covid-severityCommunity100%

A repository for code relating to UdeM ift6759 term project

11Python, Jupyter NotebookJan 13, 20231 years ago

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Cite this paper

@article{lara2025diagnosing,
  title  = {Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures},
  author = {Luis Lara and Lucia Eve Berger and Rajesh Raju},
  year   = {2025},
  eprint = {2502.16622},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.16622v3}
}

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