  The global outbreak of the novel corona virus (COVID-19) disease has
drastically impacted the world and led to one of the most challenging crisis
across the globe since World War II. The early diagnosis and isolation of
COVID-19 positive cases are considered as crucial steps towards preventing the
spread of the disease and flattening the epidemic curve. Chest Computed
Tomography (CT) scan is a highly sensitive, rapid, and accurate diagnostic
technique that can complement Reverse Transcription Polymerase Chain Reaction
(RT-PCR) test. Recently, deep learning-based models, mostly based on
Convolutional Neural Networks (CNN), have shown promising diagnostic results.
CNNs, however, are incapable of capturing spatial relations between image
instances and require large datasets. Capsule Networks, on the other hand, can
capture spatial relations, require smaller datasets, and have considerably
fewer parameters. In this paper, a Capsule network framework, referred to as
the "CT-CAPS", is presented to automatically extract distinctive features of
chest CT scans. These features, which are extracted from the layer before the
final capsule layer, are then leveraged to differentiate COVID-19 from
Non-COVID cases. The experiments on our in-house dataset of 307 patients show
the state-of-the-art performance with the accuracy of 90.8%, sensitivity of
94.5%, and specificity of 86.0%.
