  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 98% which outperforms the previous
works.
