A hierarchical nonparametric Bayesian approach for medical images and gene expressions classification

Elguebaly, Tarek; Bouguila, Nizar

doi:10.1007/s00500-014-1242-8

Cited by 18 publications

(19 citation statements)

References 55 publications

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“…A difficult aspect when considering finite mixture models is the determining of the exact component numbers, which help to avoid the issues of over-and under-fitting and minimizing the approximation errors, especially when we are trying to model complex real-world data problems (such as multimodal data) [9]. For instance, Laplace and Normal densities fail to fit many complex shapes with multi-dimensional data and when describing the heavier tails caused by specific patterns [10,11]. In this context, non-Gaussian data modelling plays an essential role in accurate data clustering and classification.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…In this context, non-Gaussian data modelling plays an essential role in accurate data clustering and classification. This problem can be addressed with more flexible statistical models, such as generalized Gaussian mixtures [11]. For this purpose, extensive research efforts have been developed; nevertheless, many of them still fail to achieve high accuracy for many applications [12].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

et al. 2021

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This paper addresses the problem of data vectors modeling, classification and recognition using infinite mixture models, which have been shown to be an effective alternative to finite mixtures in terms of selecting the optimal number of clusters. In this work, we propose a novel approach for localized features modelling using an infinite mixture model based on multivariate generalized Normal distributions (inMGNM). The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components. Robust descriptors are derived from encoding features with the Fisher vector method, which considers higher order statistics. These descriptors are combined with a linear support vector machine classifier in order to achieve higher accuracy. The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification and human activity categorization.

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Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

et al. 2021

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“…The importance of this field of research is highlighted by the publication of many papers for more than 20 years. [16][17][18][19]26,27,29,32,54,67 For instance, the classification of MRI images into normal or abnormal has been broadly studied in the literature and several interesting articles presenting various methods have been published. 26 A CNN-based method using 3*3 kernels driven by a deeper architecture for high-grade glioma segmentation in MRI images is proposed in Reference 54.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Alroobaea

Rubaiee

Bourouis

et al. 2019

Int J Imaging Syst Tech

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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 for bounded generalized Gaussian mixture models is developed. The consideration of bounded mixture models is encouraged by their capability to take into account the nature of the data that is compactly supported. Furthermore, the consideration of Bayesian inference is more attractive compared to frequentist reasoning. In this work, we address main issues related to accurate data classification such as the effective estimation of the model's parameters and the selection of the optimal model complexity. Moreover, the problem of over-or under-fitting is treated by taking into account the uncertainty through introducing prior information about the model's parameters. A comparative study between different Gaussian-based models is also performed to evaluate the performance of the proposed framework. Experiments have been conducted on challenging biomedical image datasets that involve retinal images for diabetic retinopathy detection and mammograms for breast cancer detection. Obtained results are encouraging and show the benefits of our Bayesian framework. K E Y W O R D SBayesian inference, biomedical imaging, bounded mixture models, generalized Gaussian distribution, image classification, Markov chain Monte Carlo (MCMC)

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“…Many researchers developed various machine learning and data analysis methods for medical data clustering [5], classification [6], diagnosing different diseases etc. [7]. Machine learning models developed to support various medical decision making tasks.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

Kumar¹,

Gera²,

Kamakshi³

2019

IJIES

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In recent years, a number of researchers have concentrated on medical data analytics because machine intelligence in medical diagnosis is a new trend for enormous medical applications. Generally, medical datasets are massive in size, so traditional classifiers suffered from overfitting and under-fitting problem of training set. In this paper, Gradient Descent Logistic Regression (GDLR) classification method is proposed for medical data classification. The Pearson Correlation Coefficient (PCC) is used to calculate the correlation between the features. After that, Random Forest (RF) algorithm ranks the features and selects the most relevant features to improve performance of the medical data classification. The regression technique processes the features effective and analyse the feature importance based on the weight values. The Random Forest (RF) assigns the features importance in the tree structure. The random forest is used to select the features and features are applied for the GDLR to classify effectively. The GDLR method further analysis the features for effectively analysis the feature importance based on the weight values and more relevant features are identified than the RF. The experimental analysis demonstrated that the performance of GDLR algorithm achieved better than traditional methods Neural Network for Threshold Selection (NNTS) and Mean Selection (MS). The accuracy of the proposed GDLR method achieved as 97.5% in the Hepatitis dataset, while existing mean selection method has the accuracy of 82.58%.

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A hierarchical nonparametric Bayesian approach for medical images and gene expressions classification

Cited by 18 publications

References 55 publications

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Feature Selection Effects on Gradient Descent Logistic Regression for Medical Data Classification

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