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This paper explores how well deep learning models trained on chest CT imagescan diagnose COVID-19 infected people in a fast and automated process. To thisend, we adopt advanced deep network architectures and propose a transferlearning strategy using custom-sized input tailored for each deep architectureto achieve the best performance. We conduct extensive sets of experiments ontwo CT image datasets, namely the SARS-CoV-2 CT-scan and the COVID19-CT. Theobtained results show superior performances for our models compared withprevious studies, where our best models achieve average accuracy, precision,sensitivity, specificity and F1 score of 99.4%, 99.6%, 99.8%, 99.6% and 99.4%on the SARS-CoV-2 dataset; and 92.9%, 91.3%, 93.7%, 92.2% and 92.5% on theCOVID19-CT dataset, respectively. Furthermore, we apply two visualizationtechniques to provide visual explanations for the models' predictions. Thevisualizations show well-separated clusters for CT images of COVID-19 fromother lung diseases, and accurate localizations of the COVID-19 associatedregions.

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning