  The rapid outbreak of COVID-19 threatens humans life all around the world.
Due to insufficient diagnostic infrastructures, developing an accurate,
efficient, inexpensive, and quick diagnostic tool is of great importance. As
chest radiography, such as chest X-ray (CXR) and CT computed tomography (CT),
is a possible way for screening COVID-19, developing an automatic image
classification tool is immensely helpful for detecting the patients with
COVID-19. To date, researchers have proposed several different screening
methods; however, none of them could achieve a reliable and highly sensitive
performance yet. The main drawbacks of current methods are the lack of having
enough training data, low generalization performance, and a high rate of
false-positive detection. To tackle such limitations, this study firstly builds
a large-size publicly available CT-scan dataset, consisting of more than 13k
CT-images of more than 1000 individuals, in which 8k images are taken from 500
patients infected with COVID-19. Secondly, we propose a deep learning model for
screening COVID-19 using our proposed CT dataset and report the baseline
results. Finally, we extend the proposed CT model for screening COVID-19 from
CXR images using a transfer learning approach. The experimental results show
that the proposed CT and CXR methods achieve the AUC scores of 0.886 and 0.984
respectively.
