Abstract
The COVID-19 is a highly contagious viral infection which played havoc
on everyone’s life in many different ways. According to the world health
organization and scientists, more testing potentially helps governments
and disease control organizations in containing the spread of the virus.
The use of chest radiographs is one of the early screening tests to
determine the onset of disease, as the infection affects the lungs
severely. This study will investigate and automate the process of
testing by using state-of-the-art CNN classifiers to detect the COVID19
infection. However, the viral could of many different types; therefore,
we only regard for COVID19 while the other viral infection types are
treated as non-COVID19 in the radiographs of various viral infections.
The classification task is challenging due to the limited number of
scans available for COVID19 and the minute variations in the viral
infections. We aim to employ current state-of-the-art CNN architectures,
compare their results, and determine whether deep learning algorithms
can handle the crisis appropriately. All trained models are available at
https://github.com/saeed-anwar/COVID19-Baselines