  The newly discovered Corona virus Disease 2019 (COVID-19) has been globally
spreading and causing hundreds of thousands of deaths around the world as of
its first emergence in late 2019. Computed tomography (CT) scans have shown
distinctive features and higher sensitivity compared to other diagnostic tests,
in particular the current gold standard, i.e., the Reverse Transcription
Polymerase Chain Reaction (RT-PCR) test. Current deep learning-based algorithms
are mainly developed based on Convolutional Neural Networks (CNNs) to identify
COVID-19 pneumonia cases. CNNs, however, require extensive data augmentation
and large datasets to identify detailed spatial relations between image
instances. Furthermore, existing algorithms utilizing CT scans, either extend
slice-level predictions to patient-level ones using a simple thresholding
mechanism or rely on a sophisticated infection segmentation to identify the
disease. In this paper, we propose a two-stage fully-automated CT-based
framework for identification of COVID-19 positive cases referred to as the
"COVID-FACT". COVID-FACT utilizes Capsule Networks, as its main building blocks
and is, therefore, capable of capturing spatial information. In particular, to
make the proposed COVID-FACT independent from sophisticated segmentation of the
area of infection, slices demonstrating infection are detected at the first
stage and the second stage is responsible for classifying patients into COVID
and non-COVID cases. COVID-FACT detects slices with infection, and identifies
positive COVID-19 cases using an in-house CT scan dataset, containing COVID-19,
community acquired pneumonia, and normal cases. Based on our experiments,
COVID-FACT achieves an accuracy of 90.82%, a sensitivity of 94.55%, a
specificity of 86.04%, and an Area Under the Curve (AUC) of 0.98, while
depending on far less supervision and annotation, in comparison to its
counterparts.
