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

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.

arXiv commentUpon reflection, the final version of this work does not meet the author's personal standards for thoroughness and clarity. As a result, the authors have chosen to withdraw the paper to prevent the dissemination of work that may not fully reflect the level of quality they strive to maintain