IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-031-33183-1_8.html
   My bibliography  Save this book chapter

Coronavirus Lung Image Classification with Uncertainty Estimation Using Bayesian Convolutional Neural Networks

In: Mathematical Modeling and Intelligent Control for Combating Pandemics

Author

Listed:
  • Mfundo Monchwe

    (Sol Plaatje University)

  • Ibidun C. Obagbuwa

    (Sol Plaatje University)

  • Alfred Mwanza

    (Sol Plaatje University)

Abstract

Previous attempts to identify or predict coronavirus using lung imaging data have yet to incorporate a way to quantify the uncertainty in their predictions. Additionally, these models need more certainty quantification to raise questions about their reliability. This chapter addresses these issues by modeling a coronavirus classification model that utilizes a Bayesian convolutional neural networks (BCNNs) approach. This probabilistic machine learning approach allows for the estimation of uncertainty, providing insight into the reliability of coronavirus image classification. The model’s accuracy is tested with a comprehensive radiographical lung image dataset, revealing its capability to deliver significant uncertainty information. Furthermore, comparisons with standard CNN models are conducted, highlighting the improved performance of the BCNN model in identifying complex cases that require further inspections.

Suggested Citation

  • Mfundo Monchwe & Ibidun C. Obagbuwa & Alfred Mwanza, 2023. "Coronavirus Lung Image Classification with Uncertainty Estimation Using Bayesian Convolutional Neural Networks," Springer Optimization and Its Applications, in: Zakia Hammouch & Mohamed Lahby & Dumitru Baleanu (ed.), Mathematical Modeling and Intelligent Control for Combating Pandemics, pages 129-153, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-33183-1_8
    DOI: 10.1007/978-3-031-33183-1_8
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-3-031-33183-1_8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.