2019
DOI: 10.1002/ima.22391
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Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Abstract: Biomedical image classification problem has attracted a lot of attention in medical engineering community and medicine applications. Accurate and automatic classification (eg, normal/abnormal or malignant/benign) has a variety of applications such as automatic decision making and is known to be very challenging. In this research, we address this problem by investigating the effectiveness of Bayesian inference methods for statistical bounded mixture models. Indeed, a novel approach termed as Bayesian learning f… Show more

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Cited by 22 publications
(13 citation statements)
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“…Therefore, a better solution especially for our case (i.e., when dealing with complex medical noisy data including COVID-19 infection) is to develop a more robust alternative based on fully Bayesian inference approach. We recall that Bayesian estimation has attracted a lot of attention for many applications [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. It is also known that the Bayesian approach may be more practical due to the existance of powerful simulation techniques like MCMC [ 29 ].…”
Section: Motivationsmentioning
confidence: 99%
“…Therefore, a better solution especially for our case (i.e., when dealing with complex medical noisy data including COVID-19 infection) is to develop a more robust alternative based on fully Bayesian inference approach. We recall that Bayesian estimation has attracted a lot of attention for many applications [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. It is also known that the Bayesian approach may be more practical due to the existance of powerful simulation techniques like MCMC [ 29 ].…”
Section: Motivationsmentioning
confidence: 99%
“…M-step : Update all parameters for the model. Θ j using Equation (5). end Allocate all vectors to their appropriate clusters using maximum posterior probability (MAP) estimate.…”
Section: Complete Algorithmmentioning
confidence: 99%
“…Nowadays, the trend of data mining applications in healthcare is remarkable as there are huge datasets in this sector that require a deep analysis by effective model-based techniques derived from artificial intelligence and machine learning areas. Large scale artificial intelligence and machine learning tools are increasingly successful in image-based diagnosis and have been employed for medical decision making [ 1 , 2 , 3 , 4 , 5 ] and other complex problems like scene and web pages categorization [ 6 , 7 , 8 ], retinal images classification [ 9 ] and action recognition [ 10 ]. The manual processing of these tasks is difficult, tedious and time consuming and so it is important to move to automatic methods, which are able to learn models from labeled and non-labeled data and allow faster and more accurate decisions.…”
Section: Introductionmentioning
confidence: 99%
“…It is noted that, only few works have investigated the Liouville distributions in the past such as with Beta-Liouville (IBL) mixture in [4], hence more in-depth exploitation with these distributions is needed to directly and accurately model data. Accordingly, in this paper, we are first motivated by developing a mixture model based on IBL, then we are interested by the promising results obtained recently by Bayesian inference approaches such as with Markov chain Monte Carlo (MCMC) and Metropolis-Hasting algorithms to learn various mixture models [16]- [19]. MCMC is one of the most powerful Bayesian learning sampling algorithms that outperforms deterministic methods (for more details on MCMC the reader could refer to [17]).…”
Section: Introductionmentioning
confidence: 99%