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

Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images