Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Version 1 : Received: 12 October 2020 / Approved: 14 October 2020 / Online: 14 October 2020 (09:07:51 CEST)

How to cite: Tabrizchi, H.; Mosavi, A.; Szabo-Gali, A.; Nadai, L. Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans. Preprints 2020, 2020100290 (doi: 10.20944/preprints202010.0290.v1). Tabrizchi, H.; Mosavi, A.; Szabo-Gali, A.; Nadai, L. Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans. Preprints 2020, 2020100290 (doi: 10.20944/preprints202010.0290.v1).

Abstract

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

Subject Areas

COVID-19; image-based diagnosis; artificial intelligence; machine learning; deep learning; computerized tomography; coronavirus disease

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