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

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.

@article{lara2025_2502.16622,
  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 },
  journal={arXiv preprint arXiv:2502.16622},
  year={ 2025 }
}