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Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different dis...

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Autores principales: Tchito Tchapga, Christian, Mih, Thomas Attia, Tchagna Kouanou, Aurelle, Fozin Fonzin, Theophile, Kuetche Fogang, Platini, Mezatio, Brice Anicet, Tchiotsop, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191587/
https://www.ncbi.nlm.nih.gov/pubmed/34122785
http://dx.doi.org/10.1155/2021/9998819
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author Tchito Tchapga, Christian
Mih, Thomas Attia
Tchagna Kouanou, Aurelle
Fozin Fonzin, Theophile
Kuetche Fogang, Platini
Mezatio, Brice Anicet
Tchiotsop, Daniel
author_facet Tchito Tchapga, Christian
Mih, Thomas Attia
Tchagna Kouanou, Aurelle
Fozin Fonzin, Theophile
Kuetche Fogang, Platini
Mezatio, Brice Anicet
Tchiotsop, Daniel
author_sort Tchito Tchapga, Christian
collection PubMed
description In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow.
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spelling pubmed-81915872021-06-11 Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms Tchito Tchapga, Christian Mih, Thomas Attia Tchagna Kouanou, Aurelle Fozin Fonzin, Theophile Kuetche Fogang, Platini Mezatio, Brice Anicet Tchiotsop, Daniel J Healthc Eng Review Article In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow. Hindawi 2021-05-30 /pmc/articles/PMC8191587/ /pubmed/34122785 http://dx.doi.org/10.1155/2021/9998819 Text en Copyright © 2021 Christian Tchito Tchapga et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Tchito Tchapga, Christian
Mih, Thomas Attia
Tchagna Kouanou, Aurelle
Fozin Fonzin, Theophile
Kuetche Fogang, Platini
Mezatio, Brice Anicet
Tchiotsop, Daniel
Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
title Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
title_full Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
title_fullStr Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
title_full_unstemmed Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
title_short Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms
title_sort biomedical image classification in a big data architecture using machine learning algorithms
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191587/
https://www.ncbi.nlm.nih.gov/pubmed/34122785
http://dx.doi.org/10.1155/2021/9998819
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