基于模式识别方法的肺癌分型比较  

Comparision of Lung Cancer Grouping Based on Pattern Recognition

作　　者：刘露[1,2] 马俊雷[1] 李云[3] 董永庆 刘宛予[2] 

机构地区：[1]哈尔滨理工大学自动化学院,黑龙江哈尔滨150080 [2]哈尔滨工业大学HIT-INSA中法生物医学图像联合研究中心,黑龙江哈尔滨150001 [3]中国联合网络通信有限公司北京市分公司,北京100052 [4]吉林省长白无线转播台,吉林长白134400

出　　处：《现代电子技术》2010年第10期83-85,共3页Modern Electronics Technique

基　　金：国家自然科学基金项目(60777004);黑龙江省教育厅科技计划项目(11531048);哈尔滨市科技创新人才研究专项资金项目(2008RFQXS062)

摘　　要：根据不同特征对分型准确率的影响,使用Logistic回归分析进行特征选择及优选实验研究,并采用神经网络和支持向量机方法对常见的周围型肺癌进行分型比较。通过实验,说明了神经网络和支持向量机在肺癌分型的应用方法,比较了两种模式识别方法在肺癌分型中的运用情况,验证了支持向量机在小样本情况下比神经网络具有更强的泛化能力。Taking account of the influence of different features on the grouping accuracy,the charactesistic selection and optimal experiment were performed by adopting the logistic regression analysis method,and the grouping comparison of the common peripheral lung cancer was carried out by methods of neural network and support vector machine.During the experiments,the application of both the neural network and the support vector Machine methods was adopted,and also the two methods in the application of lung cancer grouping were compared.The experimental results prove that under condition of small sample,the support vector machine method has a stronger generalizability than the neural network method.

关 键 词：肺癌分型 支持向量机 神经网络 LOGISTIC回归 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]