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Convolutional support vector models: prediction of coronavirus disease using chest X-rays

Author

Listed:
  • Maia, Mateus
  • Pimentel, Jonatha S.
  • Pereira, Ivalbert S.
  • Gondim, João
  • Barreto, Marcos E.
  • Ara, Anderson

Abstract

The disease caused by the new coronavirus (COVID-19) has been plaguing the world for months and the number of cases are growing more rapidly as the days go by. Therefore, finding a way to identify who has the causative virus is impressive, in order to find a way to stop its proliferation. In this paper, a complete and applied study of convolutional support machines will be presented to classify patients infected with COVID-19 using X-ray data and comparing them with traditional convolutional neural network (CNN). Based on the fitted models, it was possible to observe that the convolutional support vector machine with the polynomial kernel (CSVMPol) has a better predictive performance. In addition to the results obtained based on real images, the behavior of the models studied was observed through simulated images, where it was possible to observe the advantages of support vector machine (SVM) models.

Suggested Citation

  • Maia, Mateus & Pimentel, Jonatha S. & Pereira, Ivalbert S. & Gondim, João & Barreto, Marcos E. & Ara, Anderson, 2020. "Convolutional support vector models: prediction of coronavirus disease using chest X-rays," LSE Research Online Documents on Economics 115769, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:115769
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    File URL: http://eprints.lse.ac.uk/115769/
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    References listed on IDEAS

    as
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    2. Chen, Huazhou & Chen, An & Xu, Lili & Xie, Hai & Qiao, Hanli & Lin, Qinyong & Cai, Ken, 2020. "A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources," Agricultural Water Management, Elsevier, vol. 240(C).
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    More about this item

    Keywords

    Covid-19; X-ray; CNN; SVM; convolution; coronavirus;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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