Open Access

ARTICLE

Comparative Analysis of COVID-19 Detection Methods Based on Neural Network

by Inès Hilali-Jaghdam^1,*, Azhari A Elhag², Anis Ben Ishak³, Bushra M. Elamin Elnaim⁴, Omer Eltag Mohammed Elhag⁵, Feda Muhammed Abuhaimed¹, S. Abdel-Khalek^2,6

1 Computer Sciences and Information Technology Programs, Applied College, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
2 Department of Mathematics and Statistics, College of Science, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
3 Department of Quantitative Methods, Higher Institute of Management, University of Tunis, 1073, Tunisia
4 Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia
5 Department of Computer Science, Faculty of Science and Arts, King Khalid University, Abha, 61421, Saudi Arabia
6 Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt

* Corresponding Author: Inès Hilali-Jaghdam. Email:

Computers, Materials & Continua 2023, 76(1), 1127-1150. https://doi.org/10.32604/cmc.2023.038915

Received 03 January 2023; Accepted 16 March 2023; Issue published 08 June 2023

Abstract

In 2019, the novel coronavirus disease 2019 (COVID-19) ravaged the world. As of July 2021, there are about 192 million infected people worldwide and 4.1365 million deaths. At present, the new coronavirus is still spreading and circulating in many places around the world, especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic again. The symptoms of COVID-19 are diverse, and most patients have mild symptoms, with fever, dry cough, and fatigue as the main manifestations, and about 15.7% to 32.0% of patients will develop severe symptoms. Patients are screened in hospitals or primary care clinics as the initial step in the therapy for COVID-19. Although transcription-polymerase chain reaction (PCR) tests are still the primary method for making the final diagnosis, in hospitals today, the election protocol is based on medical imaging because it is quick and easy to use, which enables doctors to diagnose illnesses and their effects more quickly3. According to this approach, individuals who are thought to have COVID-19 first undergo an X-ray session and then, if further information is required, a CT-scan session. This methodology has led to a significant increase in the use of computed tomography scans (CT scans) and X-ray pictures in the clinic as substitute diagnostic methods for identifying COVID-19. To provide a significant collection of various datasets and methods used to diagnose COVID-19, this paper provides a comparative study of various state-of-the-art methods. The impact of medical imaging techniques on COVID-19 is also discussed.

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