CAJ | 학술논문

为了提高脑CT图像的分类正确率，针对分类器中的最小二乘支持向量机（LSSVM）参数优化问题，提出一种改进和声搜索算法优化LSSVM的脑CT图像分类模型（IHS-LSSVM）。将LSSVM参数看作不同乐器的声调组合，通过和声搜索算法的“调音”找到最优参数，并在寻优过程中引入粒子群算法的最优位置更新策略，增强了算法跳出局部极小值的能力，根据最优参数建立脑CT图像分类模型，并对模型的性能进行仿真测试。仿真结果表明，相对于对比模型，IHS-LSSVM不仅提高了脑CT图像分类正确率，而且加快分类速度，是一种有效的脑CT图像分类模型。
위료제고뇌CT도상적분류정학솔，침대분류기중적최소이승지지향량궤（LSSVM）삼수우화문제，제출일충개진화성수색산법우화LSSVM적뇌CT도상분류모형（IHS-LSSVM）。장LSSVM삼수간작불동악기적성조조합，통과화성수색산법적“조음”조도최우삼수，병재심우과정중인입입자군산법적최우위치경신책략，증강료산법도출국부겁소치적능력，근거최우삼수건립뇌CT도상분류모형，병대모형적성능진행방진측시。방진결과표명，상대우대비모형，IHS-LSSVM불부제고료뇌CT도상분류정학솔，이차가쾌분류속도，시일충유효적뇌CT도상분류모형。
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.