  Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a
detrimental effect on the global economy and health. A positive chest X-ray of
infected patients is a crucial step in the battle against COVID-19. Early
results suggest that abnormalities exist in chest X-rays of patients suggestive
of COVID-19. This has led to the introduction of a variety of deep learning
systems and studies have shown that the accuracy of COVID-19 patient detection
through the use of chest X-rays is strongly optimistic. Deep learning networks
like convolutional neural networks (CNNs) need a substantial amount of training
data. Because the outbreak is recent, it is difficult to gather a significant
number of radiographic images in such a short time. Therefore, in this
research, we present a method to generate synthetic chest X-ray (CXR) images by
developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based
model called CovidGAN. In addition, we demonstrate that the synthetic images
produced from CovidGAN can be utilized to enhance the performance of CNN for
COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By
adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We
hope this method will speed up COVID-19 detection and lead to more robust
systems of radiology.
