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16 pages, 2148 KiB

Open AccessArticle

Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

by Fahd Alharithi, Ahmed Almulihi, Sami Bourouis, Roobaea Alroobaea and Nizar Bouguila

Sensors 2021, 21(7), 2450; https://doi.org/10.3390/s21072450 - 2 Apr 2021

Cited by 18 | Viewed by 2590

In this paper, we propose a novel hybrid discriminative learning approach based on shifted-scaled Dirichlet mixture model (SSDMM) and Support Vector Machines (SVMs) to address some challenging problems of medical data categorization and recognition. The main goal is to capture accurately the intrinsic nature of biomedical images by considering the desirable properties of both generative and discriminative models. To achieve this objective, we propose to derive new data-based SVM kernels generated from the developed mixture model SSDMM. The proposed approach includes the following steps: the extraction of robust local descriptors, the learning of the developed mixture model via the expectation–maximization (EM) algorithm, and finally the building of three SVM kernels for data categorization and classification. The potential of the implemented framework is illustrated through two challenging problems that concern the categorization of retinal images into normal or diabetic cases and the recognition of lung diseases in chest X-rays (CXR) images. The obtained results demonstrate the merits of our hybrid approach as compared to other methods. Full article

(This article belongs to the Special Issue Computer Vision and Machine Learning for Medical Imaging System)

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14 pages, 2059 KiB

Open AccessArticle

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

by Sami Bourouis, Abdullah Alharbi and Nizar Bouguila

J. Imaging 2021, 7(1), 7; https://doi.org/10.3390/jimaging7010007 - 10 Jan 2021

Cited by 10 | Viewed by 3445

Abstract

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. Full article

(This article belongs to the Special Issue Deep Learning in Medical Image Analysis)

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12 pages, 241 KiB

Open AccessArticle

Random Shifting and Scaling of Insurance Risks

by Enkelejd Hashorva and Lanpeng Ji

Risks 2014, 2(3), 277-288; https://doi.org/10.3390/risks2030277 - 22 Jul 2014

Cited by 7 | Viewed by 5229

Abstract

Random shifting typically appears in credibility models whereas random scaling is often encountered in stochastic models for claim sizes reflecting the time-value property of money. In this article we discuss some aspects of random shifting and random scaling of insurance risks focusing in particular on credibility models, dependence structure of claim sizes in collective risk models, and extreme value models for the joint dependence of large losses. We show that specifying certain actuarial models using random shifting or scaling has some advantages for both theoretical treatments and practical applications. Full article

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