  The outbreak of the corona virus disease (COVID-19) has changed the lives of
most people on Earth. Given the high prevalence of this disease, its correct
diagnosis in order to quarantine patients is of the utmost importance in steps
of fighting this pandemic. Among the various modalities used for diagnosis,
medical imaging, especially computed tomography (CT) imaging, has been the
focus of many previous studies due to its accuracy and availability. In
addition, automation of diagnostic methods can be of great help to physicians.
In this paper, a method based on pre-trained deep neural networks is presented,
which, by taking advantage of a cyclic generative adversarial net (CycleGAN)
model for data augmentation, has reached state-of-the-art performance for the
task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a
dataset containing 3163 images from 189 patients has been collected and labeled
by physicians. Unlike prior datasets, normal data have been collected from
people suspected of having COVID-19 disease and not from data from other
diseases, and this database is made available publicly.
