Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures
This work addresses the problem of automating severity assessment for COVID-19 patients to aid healthcare professionals, but it is incremental as it applies existing methods to a new dataset.
The study tackled predicting COVID-19 severity from chest X-ray images by merging datasets and testing transfer learning with models like DenseNet161 and ViTs, achieving 80% accuracy in classification and a mean absolute error of 0.5676 in regression.
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.