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Genetic Algorithm in Data Mining of Colorectal Images

There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image pr...

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Detalles Bibliográficos
Autores principales: Chen, Shou-Ming, Zhang, Jun-Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536457/
https://www.ncbi.nlm.nih.gov/pubmed/34691237
http://dx.doi.org/10.1155/2021/3854518
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author Chen, Shou-Ming
Zhang, Jun-Hui
author_facet Chen, Shou-Ming
Zhang, Jun-Hui
author_sort Chen, Shou-Ming
collection PubMed
description There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image processing technology to perform image analysis. At the same time, combined with the actual requirements of image detection, the gray theory model is used as the basic theory of image processing, and the image detection prediction model is constructed to predict the data. In addition, in order to study the effectiveness of the algorithm, the experiment is carried out to analyze the validity of the data of the study, and the predicted value is compared with the actual value. The research shows that the proposed algorithm has certain accuracy and can provide theoretical reference for subsequent related research.
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spelling pubmed-85364572021-10-23 Genetic Algorithm in Data Mining of Colorectal Images Chen, Shou-Ming Zhang, Jun-Hui Comput Math Methods Med Research Article There is currently no effective analytical method in colorectal image analysis, which leads to certain errors in colorectal image analysis. In order to improve the accuracy of colorectal imaging detection, this study used a genetic algorithm as the data mining algorithm and combined it with image processing technology to perform image analysis. At the same time, combined with the actual requirements of image detection, the gray theory model is used as the basic theory of image processing, and the image detection prediction model is constructed to predict the data. In addition, in order to study the effectiveness of the algorithm, the experiment is carried out to analyze the validity of the data of the study, and the predicted value is compared with the actual value. The research shows that the proposed algorithm has certain accuracy and can provide theoretical reference for subsequent related research. Hindawi 2021-10-15 /pmc/articles/PMC8536457/ /pubmed/34691237 http://dx.doi.org/10.1155/2021/3854518 Text en Copyright © 2021 Shou-Ming Chen and Jun-Hui Zhang. 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 Research Article
Chen, Shou-Ming
Zhang, Jun-Hui
Genetic Algorithm in Data Mining of Colorectal Images
title Genetic Algorithm in Data Mining of Colorectal Images
title_full Genetic Algorithm in Data Mining of Colorectal Images
title_fullStr Genetic Algorithm in Data Mining of Colorectal Images
title_full_unstemmed Genetic Algorithm in Data Mining of Colorectal Images
title_short Genetic Algorithm in Data Mining of Colorectal Images
title_sort genetic algorithm in data mining of colorectal images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536457/
https://www.ncbi.nlm.nih.gov/pubmed/34691237
http://dx.doi.org/10.1155/2021/3854518
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