Classification of COVID-19 in Chest X-Ray Images using Deep Transfer Learning

  • Roaa Abdalrhman Department of Computer Science, University of Gezira, Wad Madani, Sudan
  • Murtada Elbashir Department of Computer Science, University of Gezira, Wad Madani, Sudan
  • Gais Alhadi Babikir Department of Computer Science, University of Gezira, Wad Madani, Sudan

Abstract

In December 2019, the novel coronavirus appeared in Wuhan, China, and became a critical public health problem worldwide. The transmission of this virus via small droplets produced by coughing, sneezing, and talking led to the rapid spread of the virus. Noteworthy, the coronavirus caused a devastating effect on daily lives, public health, the global economy and still threatening the lives of billions of people. Therefore, a fast and accurate method of diagnosing COVID-19 infection is vital to prevent the spread of the disease and to quickly treat affected patients. In this paper, we proposed a deep learning model for classifying covid-19 chest X-ray images into six classes. However, the main challenges are there is no large enough covid-19 dataset in the public domain compared to other classes. Hence, it is not easy to distinguish the similarities between categories and detailed features. Therefore, to counteract the problem of insufficient annotated images of covid-19 compatible with other classes, transfer learning is used which is also an effective deep feature extractor to extract similarity features between these classes.  In fact, we trained three pre-training models [RestNet50, MobileNet, ResNet101] to classify covid-19 X-ray images into six classes. The experimental results showed the validity and efficiency of our proposed model which exceeds all proposed models in the literature.

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Published
2022-10-17
How to Cite
ABDALRHMAN, Roaa; ELBASHIR, Murtada; ALHADI BABIKIR, Gais. Classification of COVID-19 in Chest X-Ray Images using Deep Transfer Learning. Gezira Journal of Engineering and Applied Sciences, [S.l.], v. 16, n. 1, p. 1-6, oct. 2022. ISSN 1858-5698. Available at: <http://journals.uofg.edu.sd/index.php/gjeas/article/view/2161>. Date accessed: 04 dec. 2022.
Section
Articles