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.
67bde94fc45e6063fecbcf04
Luis Lara
extracted_confirmed
2025-02-25T16:05:37.367Z
63e972f1ccae1fe5c6211759
https://cdn-avatars.huggingface.co/v1/production/uploads/63e972f1ccae1fe5c6211759/AfKPgMdAraUtvbtJpoHFY.jpeg
Luis Lara
user
ludolara
67bde94fc45e6063fecbcf05
Lucia Eve Berger
67bde94fc45e6063fecbcf06
Rajesh Raju
admin_assigned
2025-02-25T16:40:11.167Z
6544d303629ca3a19924cebe
/avatars/3595f9ee3b745212ceeb19be6723c7b2.svg
Rajesh Raju
user
rajeshraju
67bde94fc45e6063fecbcf07
Shawn Whitfield
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.
Transfer learning and vision transformers are evaluated for predicting COVID-19 severity from chest x-rays, with DenseNet161 and ViT models achieving high accuracy and low error.
transfer learning ImageNet CXR-pretrained models vision transformers DenseNet161 severity regression severity classification mean absolute error radiologist-predicted severity scores