  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.
