title: Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN
    Architectures
summary: 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.
paper_authors._id: 67bde94fc45e6063fecbcf04
paper_authors.name: Luis Lara
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-25T16:05:37.367Z
paper_authors._id: 63e972f1ccae1fe5c6211759
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63e972f1ccae1fe5c6211759/AfKPgMdAraUtvbtJpoHFY.jpeg
paper_authors.fullname: Luis Lara
paper_authors.type: user
paper_authors.user: ludolara
paper_authors._id: 67bde94fc45e6063fecbcf05
paper_authors.name: Lucia Eve Berger
paper_authors._id: 67bde94fc45e6063fecbcf06
paper_authors.name: Rajesh Raju
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:40:11.167Z
paper_authors._id: 6544d303629ca3a19924cebe
paper_authors.avatarUrl: /avatars/3595f9ee3b745212ceeb19be6723c7b2.svg
paper_authors.fullname: Rajesh Raju
paper_authors.type: user
paper_authors.user: rajeshraju
paper_authors._id: 67bde94fc45e6063fecbcf07
paper_authors.name: Shawn Whitfield
paper_title: Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN
    Architectures
paper_summary: 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.
paper_ai_keywords: transfer learning ImageNet CXR-pretrained models vision transformers (ViTs) DenseNet161 severity prediction classification tasks regression tasks mean absolute error control models radiologist-predicted severity scores