An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia-Reference-Cited by-同舟云学术

An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Published:2023-03-17 Issue:1 Volume:13 Page:1-16
ISSN:1947-959X
Container-title:International Journal of Service Science, Management, Engineering, and Technology
language:ng
Short-container-title:

Author:

Waleed Jumana¹,Azar Ahmad Taher²^ORCID,Albawi Saad³,Al-Azzawi Waleed Khaild⁴,Ibraheem Ibraheem Kasim⁵^ORCID,Alkhayyat Ahmed⁶,Hameed Ibrahim A.⁷,Kamal Nashwa Ahmad⁸

Affiliation:

1. Department of Computer Science, College of Science, University of Diyala, Iraq

2. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

3. College of Engineering, University of Diyala, Iraq

4. Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq

5. Computer Engineering Techniques Department, Al-Mustaqbal University College, Hilla, Iraq

6. Department of Computer Technical Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq

7. Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Alesund, Norway

8. Faculty of Engineering, Cairo University, Giza, Egypt

Abstract

Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing; however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases.

Publisher

IGI Global

Subject

Multidisciplinary,General Engineering,General Business, Management and Accounting,General Computer Science

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