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.
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Monchwe, M., Obagbuwa, I.C., Mwanza, A. (2023). Coronavirus Lung Image Classification with Uncertainty Estimation Using Bayesian Convolutional Neural Networks. In: Hammouch, Z., Lahby, M., Baleanu, D. (eds) Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer Optimization and Its Applications, vol 203. Springer, Cham. https://doi.org/10.1007/978-3-031-33183-1_8
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