A DEEP LEARNING APPROACH FED BY CT SCANS FOR DIAGNOSIS OF COVID-19

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A DEEP LEARNING APPROACH FED BY CT SCANS FOR DIAGNOSIS OF COVID-19

Abstract

Since the beginning of 2020 Covid-19 disease has widely spread around the world. To both fight disease and slow the outbreak, early detection of infected individuals is one of the most effective ways. Although the coronavirus has many symptoms, the fatal one is the damage to the lung. One of the methods used to detect this damage is computed tomography (CT). Convolutional Neural Networks (CNNs), which is a highly effective deep learning algorithm on multidimensional data, is used on different types of medical images such as CT scan, MRI, and X-ray. In this study, we aim to develop a deep learning approach and thus detect/diagnose COVID-19 by using chest CT scans.  For this purpose, a public resource consisting of 349 CT scans of 216 patients with COVID-19 clinical findings and CT scans of 397 healthy individuals was used. Diagnostic performance was assessed by accuracy, precision, recall, Matthews’s coefficient correlation (MCC), and F-measure criteria. The validity of the approach was tested using a 10-fold cross-validation technique. The results showed that CNN achieves an average accuracy of 92.63%, precision 92.95%, recall 93.18%, MCC %85.20, and F1-measure 93.06%. Considering the results obtained with this approach developed within the scope of this study, the mentioned approach may be an alternative or supportive of classical diagnostic approaches in coronavirus outbreak.

Keywords: COVID-19, deep learning, diagnosis, CT-scan, classification