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

• URL: http://arxiv.org/abs/2502.16622v3
• Date: Fri, 28 Feb 2025 14:34:45 GMT
• Title: Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN Architectures
• Abstract summary: We produce a large COVID severity dataset by merging three sources.<n>A pretrained DenseNet161 model performed the best on the three class severity prediction problem.<n>The ViT had the best regression results, with a mean absolute error of 0.5676 compared to radiologist-predicted severity scores.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays [1.1470070927586018]
  This study presents a comparative evaluation of seven deep learning architectures for multi-class chest disease classification.<n> ConvNeXt-Tiny achieved the highest performance (92.31% accuracy, 95.70% AUROC), while MobileNetV2 emerged as the most parameter-efficient model (3.5M parameters, 90.42% accuracy, 94.10% AUROC)<n>These findings demonstrate that high-accuracy multi-disease chest X-ray classification is achievable without excessive computational resources.
  arXiv  Detail & Related papers  (2026-03-11T07:52:36Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis [37.41181188499616]
  Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression. Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training. A U-Net-based model was developed to identify a clinically relevant region of the CXR.
  arXiv  Detail & Related papers  (2022-10-11T13:30:34Z)
• A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT [0.0]
  The aim of this study was to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958.
  arXiv  Detail & Related papers  (2022-06-15T18:35:22Z)
• COVIDx CXR-3: A Large-Scale, Open-Source Benchmark Dataset of Chest X-ray Images for Computer-Aided COVID-19 Diagnostics [69.55060769611916]
  The use of chest X-ray (CXR) imaging as a complementary screening strategy to RT-PCR testing is increasing. Many visual perception models have been proposed for COVID-19 screening based on CXR imaging. We introduce COVIDx CXR-3, a large-scale benchmark dataset of CXR images for supporting COVID-19 computer vision research.
  arXiv  Detail & Related papers  (2022-06-08T04:39:44Z)
• COVID-19 Classification of X-ray Images Using Deep Neural Networks [36.99143569437537]
  The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19. A machine learning model was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve.
  arXiv  Detail & Related papers  (2020-10-03T13:57:08Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images [0.0]
  Currently, Coronavirus disease (COVID-19) is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. Deep learning neural networks have a great potential for building COVID-19 triage systems. This paper is three-fold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Cl'inico San Cecilio, Granada, Spain, we built COVIDGR-1.0, and (iii) we propose COVID Smart Data based Network (COVID-SD
  arXiv  Detail & Related papers  (2020-06-02T06:18:34Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation [95.57532063232198]
  coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries. To control the infection, identifying and separating the infected people is the most crucial step. This paper develops a novel Joint Classification and (JCS) system to perform real-time and explainable COVID-19 chest CT diagnosis.
  arXiv  Detail & Related papers  (2020-04-15T12:30:40Z)
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images [0.0]
  CoroNet is a Deep Conceptional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model achieved an overall accuracy of 89.6% and the precision and recall rate for COVID-19 cases are 93% and 98.2%.
  arXiv  Detail & Related papers  (2020-04-10T07:46:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.