  Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the
world as we know it at the end of the 2nd decade of the 21st century. COVID-19
is extremely contagious and quickly spreading globally making its early
diagnosis of paramount importance. Early diagnosis of COVID-19 enables health
care professionals and government authorities to break the chain of transition
and 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, reveal
specific manifestations associated with this disease. Overlap with other lung
infections makes human-centered diagnosis of COVID-19 challenging.
Consequently, there has been an urgent surge of interest to develop Deep Neural
Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural
Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs,
however, are prone to lose spatial information between image instances and
require large datasets. The paper presents an alternative modeling framework
based on Capsule Networks, referred to as the COVID-CAPS, being capable of
handling small datasets, which is of significant importance due to sudden and
rapid emergence of COVID-19. Our results based on a dataset of X-ray images
show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS
achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and
Area Under the Curve (AUC) of 0.97, while having far less number of trainable
parameters in comparison to its counterparts. To further improve diagnosis
capabilities of the COVID-CAPS, pre-training based on a new dataset constructed
from an external dataset of X-ray images. Pre-training with a dataset of
similar nature further improved accuracy to 98.3% and specificity to 98.6%.
