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Article

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

1
Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, P.O. Box 11099, Taif 21944, Saudi Arabia
2
The Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC H3G 1T7, Canada
*
Author to whom correspondence should be addressed.
Received: 11 November 2020 / Revised: 18 December 2020 / Accepted: 7 January 2021 / Published: 10 January 2021
(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)
Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework. View Full-Text
Keywords: infection detection; COVID-19; X-ray images; image classification; bayesian inference; shifted-scaled dirichlet distribution; MCMC; gibbs sampling infection detection; COVID-19; X-ray images; image classification; bayesian inference; shifted-scaled dirichlet distribution; MCMC; gibbs sampling
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MDPI and ACS Style

Bourouis, S.; Alharbi, A.; Bouguila, N. Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images. J. Imaging 2021, 7, 7. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010007

AMA Style

Bourouis S, Alharbi A, Bouguila N. Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images. Journal of Imaging. 2021; 7(1):7. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010007

Chicago/Turabian Style

Bourouis, Sami; Alharbi, Abdullah; Bouguila, Nizar. 2021. "Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images" J. Imaging 7, no. 1: 7. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging7010007

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