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

Mfundo Monchwe, Ibidun C. Obagbuwa () and Alfred Mwanza ()
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Mfundo Monchwe: Sol Plaatje University
Ibidun C. Obagbuwa: Sol Plaatje University
Alfred Mwanza: Sol Plaatje University

A chapter in Mathematical Modeling and Intelligent Control for Combating Pandemics, 2023, pp 129-153 from Springer

Abstract: 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.

Keywords: Computer vision; Deep learning; Covid-19; Probabilistic machine learning; Bayesian CNNs (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-33183-1_8

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DOI: 10.1007/978-3-031-33183-1_8

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