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Efficient Deep Neural Network for an Automated Detection of COVID-19 using CT images*

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the global population’s health and well-being. Successful screening of infected patients is a crucial step in the battle against COVID-19, with radiology inspection using chest radiography being one of the most popular screening methods. Early studies discovered that patients with COVID-19 infection have anomalies in chest radiography images. In this study, we present our Deep Convolutional Neural Network (CNN) for an automatic detection of COVID-19 using computed tomography (CT). Multiple models are presented and fine-tuned to provide accurate detection of COVID-19 vs. normal vs. pneumonia. The proposed model gives an Area Under Curve (AUC) of 99.64%, an accuracy (ACC) of 96.37%, a specificity of 96.00% and a sensitivity of 97.00%. Moreover, an explainability algorithm has been developed and shows the high efficiency of identifying the pathological signs of COVID-19 in CT scans.