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

Abstract: In order to improve the brain CT image classification accuracy, this paper proposes brain CT mage classification model（IHS-LSSVM） based on the least squares support vector machine and harmony search algorithm. Firstly, the LSSVM parameters are taken as different musical tone combination, and then the harmony search algorithm is used to find the optimal parameters, 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 accuracy, but also accelerates the classification speed, so it is an effective brain CT image classification model.

Key words: medical image classification, least squares support vector machines, harmony search algorithm, particle swarm optimization algorithm