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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...
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/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. |
format | Online Article Text |
id | pubmed-8536457 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chenshouming geneticalgorithmindataminingofcolorectalimages AT zhangjunhui geneticalgorithmindataminingofcolorectalimages |