摘要
为了提高宫颈细胞识别速度,以最少的特征数量获得最高的识别准确率,运用分类与回归树算法(Classification and Regression Trees,CART)进行特征的选择,并采用粒子群算法(Particle Swarm Optimization,PSO)对分类器支持向量机(Support Vector Machine,SVM)进行优化,形成了PSO-SVM分类算法对细胞进行分类.使用Herlev数据集对文中提出的算法进行验证.通过CART特征选择方法,成功地从20个特征中提取出9个更具代表性的特征,并且二分类和七分类的准确率均达到99%以上.并引入其他几种宫颈癌细胞的分类识别算法进行仿真比较,结果表明,本文算法在特征数目较少的情况下识别准确率依然具有明显优势,从而验证了该算法的有效性.所述方法有效降低了人工特征选择的难度,在减少了识别用时的情况下,依然保证了细胞的识别准确率与之前几乎无异,为宫颈癌疾病诊断提供了一套有效的方法框架.
In order to improve the recognition speed of cervical cell and obtain the highest recognition accuracy with the least number of features,this paper innovatively uses the Classification and Regression Trees(CART)algorithm to select features,and then the Particle Swarm Optimization(PSO)algorithm is used to optimize the Support Vector Machine(SVM).Therefore,the PSO-SVM classification algorithm is formed to classify the cells.This paper uses the Herlev dataset to verify the validity of the proposed algorithm.Through the CART feature selection method,9 representative features are successfully extracted from 20 features,and the accuracy of two classifications and seven classifications are above 99%.Further,this paper introduces several other classification and recognition algorithms of cervical cancer cells for simulation comparison.It can be founds that the recognition accuracy of this algorithm is obviously superior when the number of features is small,which indicates that the proposed algorithm is effective.The method effectively reduces the difficulty of artificial feature selection,and ensures that the recognition accuracy of the cells is almost the same as before when the recognition time is reduced.Thus,the proposed algorithm.
作者
董娜
赵丽
常建芳
吴爱国
DONG Na;ZHAO Li;CHANG Jianfang;WU Aiguo(School of Electrical Automation and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2019年第12期1-8,共8页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(61773282)~~
