Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI)...

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Autores principales:	Shuang Liu, Min Tang, Shuqin Ruan, Feng Wei, Jiaxi Lu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/eab6a95b372b47dc9b2f0e7b33d280b0
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spelling	oai:doaj.org-article:eab6a95b372b47dc9b2f0e7b33d280b02021-11-15T01:19:21ZValue of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms1875-919X10.1155/2021/5289128https://doaj.org/article/eab6a95b372b47dc9b2f0e7b33d280b02021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5289128https://doaj.org/toc/1875-919XThis study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM (P<0.05). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants (P<0.05). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups (P<0.05). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.Shuang LiuMin TangShuqin RuanFeng WeiJiaxi LuHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Shuang Liu Min Tang Shuqin Ruan Feng Wei Jiaxi Lu Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms
description	This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM (P<0.05). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants (P<0.05). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups (P<0.05). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.
format	article
author	Shuang Liu Min Tang Shuqin Ruan Feng Wei Jiaxi Lu
author_facet	Shuang Liu Min Tang Shuqin Ruan Feng Wei Jiaxi Lu
author_sort	Shuang Liu
title	Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms
title_short	Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms
title_full	Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms
title_fullStr	Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms
title_full_unstemmed	Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms
title_sort	value of magnetic resonance imaging features in diagnosis and treatment of breast cancer under intelligent algorithms
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/eab6a95b372b47dc9b2f0e7b33d280b0
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Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

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