biostudies-literatureUnknown10Mondal AK<i>Objective:</i> Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. <i>MethIEEE journal of translational engineering in health and medicine1100110https://www.ebi.ac.uk/biostudies/studies/S-EPMC8691725biostudies-literaturexViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.PMC8691725Bhattacharjee AMondal AKSingla PPrathosh APfalsexViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography.<i>Objective:</i> Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19) across the globe has pushed the health care system in many countries to the verge of collapse. Therefore, it is imperative to correctly identify COVID-19 positive patients and isolate them as soon as possible to contain the spread of the disease and reduce the ongoing burden on the healthcare system. The primary COVID-19 screening test, RT-PCR although accurate and reliable, has a long turn-around time. In the recent past, several researchers have demonstrated the use of Deep Learning (DL) methods on chest radiography (such as X-ray and CT) for COVID-19 detection. However, existing CNN based DL methods fail to capture the global context due to their inherent image-specific inductive bias. <i>Meth2022-01-01T00:00:00Z20222025-05-29T22:25:23.075Z2025-04-04T03:19:37.485ZS-EPMC86917253495674110.1109/JTEHM.2021.3134096