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Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed theworld as we know it at the end of the 2nd decade of the 21st century. COVID-19is extremely contagious and quickly spreading globally making its earlydiagnosis of paramount importance. Early diagnosis of COVID-19 enables healthcare professionals and government authorities to break the chain of transitionand flatten the epidemic curve. The common type of COVID-19 diagnosis test,however, requires specific equipment and has relatively low sensitivity.Computed tomography (CT) scans and X-ray images, on the other hand, revealspecific manifestations associated with this disease. Overlap with other lunginfections makes human-centered diagnosis of COVID-19 challenging.Consequently, there has been an urgent surge of interest to develop Deep NeuralNetwork (DNN)-based diagnosis solutions, mainly based on Convolutional NeuralNetworks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs,however, are prone to lose spatial information between image instances andrequire large datasets. The paper presents an alternative modeling frameworkbased on Capsule Networks, referred to as the COVID-CAPS, being capable ofhandling small datasets, which is of significant importance due to sudden andrapid emergence of COVID-19. Our results based on a dataset of X-ray imagesshow that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPSachieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, andArea Under the Curve (AUC) of 0.97, while having far less number of trainableparameters in comparison to its counterparts. To further improve diagnosiscapabilities of the COVID-CAPS, pre-training based on a new dataset constructedfrom an external dataset of X-ray images. Pre-training with a dataset ofsimilar nature further improved accuracy to 98.3% and specificity to 98.6%.

COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images