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COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model

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Applied Intelligence and Informatics (AII 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1435))

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Abstract

AI-based medical image processing has made significant progress, and it has a significant impact on biomedical research. Among the imaging variants, Chest x-rays imaging is cheap, simple, and can be used to detect influenza, tuberculosis, and various other illnesses. Researchers discovered that coronavirus spreads through the lungs, causing severe injuries during the COVID19 pandemic. As a result, chest x-rays can be used to detect COVID-19, making it a more robust detection method. In this paper, a RegNet hierarchical deep learning-based model has been proposed to detect COVID-19 positive and negative cases using CXI. The RegNet structure is designed to develop a model with a small number of epochs and parameters. The performance measurement found that the model takes five periods to reach a total accuracy of 98.08%. To test the model, we used two sets of data. The first dataset consists of 1200 COVID-19 positive CXRs and 1,341 COVID-19 negative CXRs, and the second dataset consists of 195 COVID-19 positive CXRs and 2,000 COVID-19 negative CXRs; all of these are publicly available. We obtained precision of 99.02% and 97.13% for these datasets, respectively. As a result of this finding, the proposed approach could be used for mass screening, and, as far as we are aware, the results achieved indicate that this model could be used as a screen guide.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh

    Md. Kawsher Mahbub, Milon Biswas & Abdul Mozid Miah

  2. University of South Florida, Tampa, USA

    Ahmed Shahabaz

  3. Institute of Information Technology, Jahangimagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

Authors
  1. Md. Kawsher Mahbub
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  2. Milon Biswas
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  3. Abdul Mozid Miah
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  4. Ahmed Shahabaz
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  5. M. Shamim Kaiser
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Corresponding author

Correspondence to M. Shamim Kaiser .

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Mahbub, M.K., Biswas, M., Miah, A.M., Shahabaz, A., Kaiser, M.S. (2021). COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_28

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  • DOI: https://doi.org/10.1007/978-3-030-82269-9_28

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  • Print ISBN: 978-3-030-82268-2

  • Online ISBN: 978-3-030-82269-9

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