摘要
以微波成像作为主要手段的脑中风检测技术是一种安全便捷且成本低的微波检测方法,但由于脑部结构复杂,直接成像精度不高效果不可靠。通常采用微波提取脑部特征向量,通过分类模型处理检测脑中风的发生。对适用于多特征向量的脑中风微波检测分类模型进行研究,在构建Adaboost分类器模型基础上,采用分布式粒子群算法对模型进行优化。实验结果表明,将优化后的分类模型用于脑中风检测,精准度高达99.50%,运行速度提升46%,使得脑中风检测精准度和效率得到极大改善。
The microwave detection technology of stroke is a safe, convenient and low-cost shortwave detection method, and mainly uses the microwave imaging technology ( MIT ). However, due to the complex brain structure, the direct imaging accuracy is not high and the effect is not reliable. It usually extracts the microwave eigenvector of brain and uses the classification model to detect the presence of stroke. This paper makes an exploration on the multi-eigenvector microwave detection model of the brain stroke. It constructs an adaptive boosting (Adaboost ) model, and uses the distributed particle swarm optimization (DPSO) to optimize it. The experimental results show that the optimized classification model has an accuracy of 99.50% for stroke detection, and it improves operating speed by 46%, which greatly improves the accuracy and efficiency of stroke detection.
作者
刘强
叶建芳
吴怡之
LIU Qiang;YE Jianfang;WU Yizhi(Engineering Research Center of Digital Textile and Garment Technology, Shanghai 201620;College of Information Science & Technology, Donghua University, Shanghai 201620)
出处
《微型电脑应用》
2019年第4期12-15,共4页
Microcomputer Applications
基金
东华大学非线性科学研究所基金资助项目(20160905-3)
关键词
脑中风
微波检测
多特征向量
分类模型
分布式粒子群优化
Brain stroke
Microwave detection
Multiple eigenvector
Classification model
Distributed particle swarm optimization
