  COVID-19 spread across the globe at an immense rate has left healthcare
systems incapacitated to diagnose and test patients at the needed rate. Studies
have shown promising results for detection of COVID-19 from viral bacterial
pneumonia in chest X-rays. Automation of COVID-19 testing using medical images
can speed up the testing process of patients where health care systems lack
sufficient numbers of the reverse-transcription polymerase chain reaction
(RT-PCR) tests. Supervised deep learning models such as convolutional neural
networks (CNN) need enough labeled data for all classes to correctly learn the
task of detection. Gathering labeled data is a cumbersome task and requires
time and resources which could further strain health care systems and
radiologists at the early stages of a pandemic such as COVID-19. In this study,
we propose a randomized generative adversarial network (RANDGAN) that detects
images of an unknown class (COVID-19) from known and labelled classes (Normal
and Viral Pneumonia) without the need for labels and training data from the
unknown class of images (COVID-19). We used the largest publicly available
COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia,
and COVID-19 images from multiple public databases. In this work, we use
transfer learning to segment the lungs in the COVIDx dataset. Next, we show why
segmentation of the region of interest (lungs) is vital to correctly learn the
task of classification, specifically in datasets that contain images from
different resources as it is the case for the COVIDx dataset. Finally, we show
improved results in detection of COVID-19 cases using our generative model
(RANDGAN) compared to conventional generative adversarial networks (GANs) for
anomaly detection in medical images, improving the area under the ROC curve
from 0.71 to 0.77.
