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

EasyChair Preprint 4226

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.

Keyphrases: Artificial Intelligence, COVID-19, computerized tomography, deep learning, image-based diagnosis, machine learning

@booklet{EasyChair:4226,
  author    = {Hamed Tabrizchi and Amir Mosavi and Akos Szabo-Gali and Laszlo Nadai},
  title     = {Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans},
  howpublished = {EasyChair Preprint 4226},
  year      = {EasyChair, 2020}}