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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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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. |
format | Online Article Text |
id | pubmed-8191587 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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