COVID-19 Diagnosis and Classification from CXR Images based on Vision Transformer (ViT)

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dc.contributor.advisorAshfia Binte Habib
dc.contributor.authorMahbubur Rahman
dc.contributor.authorShihabur Rahman Samrat
dc.contributor.authorAbdullah Al Ahad
dc.contributor.id1731134042
dc.contributor.id1731574042
dc.contributor.id1731496042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025-07-10
dc.date.accessioned2025-07-10T04:44:36Z
dc.date.available2025-07-10T04:44:36Z
dc.date.issued2021
dc.description.abstractThe COVID-19 pandemic is far from over, and the current primary method of diagnosis is Reverse Transcription Polymerase Chain Reaction (RT-PCR). Although RT-PCR is reliable, it is known to have a long turnaround time and high false-negative rates that can severely hinder the accuracy of diagnosis. Alongside RT-PCR, Rapid Antigen Tests (RAT) are also used, but they have much lower accuracy than RT-PCR. Motivated by the flaws of the current diagnosis methods, we present a Vision Transformer-based classifier for the successful diagnosis and classification of COVID-19 using chest X-Ray (CXR) images. A 15000-sample CXR dataset was compiled, which consisted of 5000 CXRs per class. Afterwards, a Vision Transfer (ViT) was fine-tuned on the dataset. ResNet-50 and DenseNet121 were used as baseline models. It is observed that the Vision Transformer-based model had the highest classification accuracy of 96.2% with an F1 score of 0.965 and the average precision and recall of 0.9617 and 0.962, respectively. This study demonstrates the adequacy of the ViT for the identification and classification of COVID-19 and Pneumonia.
dc.description.degreeUndergraduate
dc.identifier.cd600000399
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1243
dc.language.isoen
dc.publisherNorth South University
dc.titleCOVID-19 Diagnosis and Classification from CXR Images based on Vision Transformer (ViT)
dc.typeProject
oaire.citation.endPage29
oaire.citation.startPage1
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