基于模式识别方法的肺癌分型比较

Comparision of Lung Cancer Grouping Based on Pattern Recognition

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.