计算机工程与应用Issue(22)：146-149,4.DOI:10.3778/j.issn.1002-8331.1304-0018
改进和声搜索算法优化LSSVM的脑CT图像分类
Brain CT image classification based on least squares support vector machine opti-mized by improved harmony search algorithm
郭正红 1赵丙辰2
作者信息
- 1. 河北北方学院 信息科学与工程学院 医学信息系，河北 张家口 075000
- 2. 邢台学院 信息科学与技术系，河北 邢台 054001
- 折叠
摘要
Abstract
In order to improve the brain CT image classification accuracy, this paper proposes brain CT mage classification mod-el(IHS-LSSVM)based on the least squares support vector machine and harmony search algorithm. Firstly, the LSSVM parame-ters are taken as different musical tone combination, and then the harmony search algorithm is used to find the optimal parame-ters, and the optimal position adjustment strategy is introduced to enhance the ability of jumping out of local minima, the brain CT image classification model is established according to the optimal parameters, and the performance of the model is tested. The simulation results show that, compared with the other models, IHS-LSSVM not only improves the image classification accu-racy, but also accelerates the classification speed, so it is an effective brain CT image classification model.关键词
脑CT图像分类/最小二乘支持向量机/和声搜索算法/粒子群优化算法Key words
medical image classification/least squares support vector machines/harmony search algorithm/particle swarm op-timization algorithm分类
信息技术与安全科学引用本文复制引用
郭正红,赵丙辰..改进和声搜索算法优化LSSVM的脑CT图像分类[J].计算机工程与应用,2013,(22):146-149,4.
