  By the start of 2020, the novel coronavirus disease (COVID-19) has been
declared a worldwide pandemic. Because of the severity of this infectious
disease, several kinds of research have focused on combatting its ongoing
spread. One potential solution to detect COVID-19 is by analyzing the chest
X-ray images using Deep Learning (DL) models. In this context, Convolutional
Neural Networks (CNNs) are presented as efficient techniques for early
diagnosis. In this study, we propose a novel randomly initialized CNN
architecture for the recognition of COVID-19. This network consists of a set of
different-sized hidden layers created from scratch. The performance of this
network is evaluated through two public datasets, which are the COVIDx and the
enhanced COVID-19 datasets. Both of these datasets consist of 3 different
classes of images: COVID19, pneumonia, and normal chest X-ray images. The
proposed CNN model yields encouraging results with 94% and 99% of accuracy for
COVIDx and enhanced COVID-19 dataset, respectively.
