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- Title
Transfer deep learning approach for detecting coronavirus disease in X-ray images.
- Authors
Al-Smadi, Mohammed; Hammad, Mahmoud; Baker, Qanita Bani; Tawalbeh, Saja Khaled; Al-Zboon, Sa’ad A.
- Abstract
Currently, the whole world is fighting a very dangerous and infectious disease caused by the novel coronavirus, called COVID-19. The COVID-19 is rapidly spreading around the world due to its high infection rate. Therefore, early discovery of COVID-19 is crucial to better treat the infected person as well as to slow down the spread of this virus. However, the current solution for detecting COVID-19 cases including the PCR test, CT images, epidemiologically history, and clinical symptoms suffer from high false positive. To overcome this problem, we have developed a novel transfer deep learning approach for detecting COVID-19 based on x-ray images. Our approach helps medical staff in determining if a patient is normal, has COVID-19, or other pneumonia. Our approach relies on pre-trained models including Inception-V3, Xception, and MobileNet to perform two tasks: i) binary classification to determine if a person infected with COVID-19 or not and ii) a multi-task classification problem to distinguish normal, COVID-19, and pneumonia cases. Our experimental results on a large dataset show that the F1-score is 100% in the first task and 97.66 in the second task.
- Subjects
DEEP learning; X-ray imaging; COVID-19 pandemic; COVID-19; SARS-CoV-2
- Publication
International Journal of Electrical & Computer Engineering (2088-8708), 2021, Vol 11, Issue 6, p4999
- ISSN
2088-8708
- Publication type
Article
- DOI
10.11591/ijece.v11i6.pp4999-5008