Genetic Algorithm in Data Mining of Colorectal Images

• Published in: Computational and mathematical methods in medicine Vol. 2021; pp. 1 - 8
• Main Authors: Chen, Shou-Ming, Zhang, Jun-Hui
• Format: Journal Article
• Language: English
• Published: United States Hindawi 15.10.2021
• Subjects: Adenocarcinoma - diagnostic imaging; Adenocarcinoma - secondary; Algorithms; Colorectal Neoplasms - diagnostic imaging; Colorectal Neoplasms - pathology; Computational Biology; Data Mining - methods; Data Mining - statistics & numerical data; Humans; Image Interpretation, Computer-Assisted - methods; Image Interpretation, Computer-Assisted - statistics & numerical data; Lymphatic Metastasis - diagnostic imaging; Rectal Neoplasms - diagnostic imaging; Rectal Neoplasms - pathology; Tomography, X-Ray Computed - statistics & numerical data
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2021/3854518

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.