Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Images

• URL: http://arxiv.org/abs/2306.06914v2
• Date: Wed, 14 Jun 2023 09:56:59 GMT
• Title: Enhancing COVID-19 Diagnosis through Vision Transformer-Based Analysis of Chest X-ray Images
• Authors: Sultan Zavrak
• Abstract summary: The research endeavor posits an innovative framework for the automated diagnosis of COVID-19, harnessing raw chest X-ray images. The developed models were appraised in terms of their binary classification performance, discerning COVID-19 from Normal cases. The proposed model evinced extraordinary precision, registering results of 99.92% and 99.84% for binary classification, 97.95% and 86.48% for ternary classification, and 86.81% for quaternary classification, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The advent of 2019 Coronavirus (COVID-19) has engendered a momentous global health crisis, necessitating the identification of the ailment in individuals through diverse diagnostic modalities. Radiological imaging, particularly the deployment of X-ray imaging, has been recognized as a pivotal instrument in the detection and characterization of COVID-19. Recent investigations have unveiled invaluable insights pertaining to the virus within X-ray images, instigating the exploration of methodologies aimed at augmenting diagnostic accuracy through the utilization of artificial intelligence (AI) techniques. The current research endeavor posits an innovative framework for the automated diagnosis of COVID-19, harnessing raw chest X-ray images, specifically by means of fine-tuning pre-trained Vision Transformer (ViT) models. The developed models were appraised in terms of their binary classification performance, discerning COVID-19 from Normal cases, as well as their ternary classification performance, discriminating COVID-19 from Pneumonia and Normal instances, and lastly, their quaternary classification performance, discriminating COVID-19 from Bacterial Pneumonia, Viral Pneumonia, and Normal conditions, employing distinct datasets. The proposed model evinced extraordinary precision, registering results of 99.92% and 99.84% for binary classification, 97.95% and 86.48% for ternary classification, and 86.81% for quaternary classification, respectively, on the respective datasets.

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