Your browser doesn't support javascript.
loading
Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans
Hamed Tabrizchi; Amir MOSAVI; Akos Szabo-Gali; Laszlo Nadai.
Affiliation
  • Hamed Tabrizchi; Department of Computer Science, Shahid Bahonar University of Kerman
  • Amir MOSAVI; Obuda University
  • Akos Szabo-Gali; John von Neumann Faculty of Infromatics, Obuda University Budapest, Hungary
  • Laszlo Nadai; Kalman Kando Faculty of Electrical Engineering, Obuda University Budapest, Hungary
Preprint in En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20248582
Journal article
A scientific journal published article is available and is probably based on this preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See journal article
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.
License
cc_by
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Language: En Year: 2020 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-MEDRXIV Language: En Year: 2020 Document type: Preprint