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COVID-19 Diagnosis Using Transfer-Learning Techniques

Mohammed Faisal1,*, Fahad Albogamy2, Hebah ElGibreen3, Mohammed Algabri3, Syed Ahad M. Alvi1, Mansour Alsulaiman3

1 College of Applied Computer Sciences, King Saud University, Riyadh, Saudi Arabia
2 Turabah University College, Taif University, Taif, Saudi Arabia
3 College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia

* Corresponding Author: Mohammed Faisal. Email: email

(This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)

Intelligent Automation & Soft Computing 2021, 29(3), 649-667.


COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis systems achieved excellent results with the VGG-19 architecture. The two-classifier diagnosis system achieved high sensitivity for COVID-19, with 99.5% and 100% on DATASET-1 and DATASET-2, respectively. The three-classifier diagnosis system achieves high sensitivity for COVID-19, with 98.4% and 100% on DATASET-1 and DATASET-2, respectively. The high sensitivity of these diagnostic systems for COVID-19 will significantly improve the speed and precision of COVID-19 diagnosis.


Cite This Article

M. Faisal, F. Albogamy, H. ElGibreen, M. Algabri, S. Ahad M. Alvi et al., "Covid-19 diagnosis using transfer-learning techniques," Intelligent Automation & Soft Computing, vol. 29, no.3, pp. 649–667, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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