AUTHOR=Marefat Abdolreza , Marefat Mahdieh , Hassannataj Joloudari Javad , Nematollahi Mohammad Ali , Lashgari Reza TITLE=CCTCOVID: COVID-19 detection from chest X-ray images using Compact Convolutional Transformers JOURNAL=Frontiers in Public Health VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1025746 DOI=10.3389/fpubh.2023.1025746 ISSN=2296-2565 ABSTRACT=

COVID-19 is a novel virus that attacks the upper respiratory tract and the lungs. Its person-to-person transmissibility is considerably rapid and this has caused serious problems in approximately every facet of individuals' lives. While some infected individuals may remain completely asymptomatic, others have been frequently witnessed to have mild to severe symptoms. In addition to this, thousands of death cases around the globe indicated that detecting COVID-19 is an urgent demand in the communities. Practically, this is prominently done with the help of screening medical images such as Computed Tomography (CT) and X-ray images. However, the cumbersome clinical procedures and a large number of daily cases have imposed great challenges on medical practitioners. Deep Learning-based approaches have demonstrated a profound potential in a wide range of medical tasks. As a result, we introduce a transformer-based method for automatically detecting COVID-19 from X-ray images using Compact Convolutional Transformers (CCT). Our extensive experiments prove the efficacy of the proposed method with an accuracy of 99.22% which outperforms the previous works.