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


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%.


Classification; COVID-19; Deep learning; ResNet backbone; SARS-CoV-2

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).