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All Journal Bulletin of Electrical Engineering and Informatics
Hassan S. Migdadi
Al-Ahliyya Amman University
Electrical & Electronics Engineering

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Deep learning algorithms to improve COVID-19 classification based on CT images Hamza Abu Owida; Hassan S. Migdadi; Omar Salah Mohamed Hemied; Nawaf Farhan Fankur Alshdaifat; Suhaila Farhan Ahmad Abuowaida; Rami S. Alkhawaldeh
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3802

Abstract

In response to the growing threat posed by COVID-19, several initiatives have been launched to develop methods of halting the progression of the disease. In order to diagnose the COVID-19 infection, testing kits were utilized; however, the use of these kits is time-consuming and suffers from a lack of quality control measures. Computed tomography is an essential part of the diagnostic process in the treatment of COVID-19 (CT). The process of disease detection and diagnosis could be sped up with the help of automation, which would cut down on the number of exams that need to be carried out. A number of recently developed deep learning tools make it possible to automate the Covid-19 scanning process in CT scans and provide additional assistance. This paper investigates how to quickly identify COVID-19 using computational tomography (CT) scans, and it does so by using a deep learning technique that is derived from improving ResNet architecture. In order to test the proposed model, COVID-19 CT scans that include a patient-based split are utilized. The accuracy of the model’s core components is 98.1%, with specificity at 97% and sensitivity at 98.6%.
Co-Authors Hamza Abu Owida Nawaf Farhan Fankur Alshdaifat Omar Salah Mohamed Hemied Rami S. Alkhawaldeh Suhaila Farhan Ahmad Abuowaida