径向基人工神经网络在宫颈细胞图像识别中的应用

目的：探讨径向基（RBF）人工神经网络在宫颈细胞图像识别中的应用。方法：提取宫颈细胞和细胞核的15个形态学特征参数及12个色度学特征参数，对700个宫颈细胞按正常、低度鳞状上皮内病变（LSIL）、高度鳞状上皮内病变（HSIL）、宫颈癌进行分类识别。利用软件STATISTICA7．0建立网络模型并训练，用VC＋＋．NET语言调用网络。结果：RBF网络对训练集的拟合度为97．3％，对测试集的分类准确率为95．4％。在测试集中，正常细胞的识别率为96％，LSIL细胞识别率为94％，HSIL细胞识别率为100％，癌细胞识别率为88％。RBF网络输入参数的敏感度排序与细胞病理学特征基本一致。结论：RBF人工神经网络可以很好的对宫颈细胞特别是HSIL细胞进行分类识别。

Application of radial basis function artificial neural network in image diagnosis of cervical cells

Objective: To investigate the possibility of applying artificial neural network based on radial basis function(RBF) to image recognition of cervical cells.Methods:According to 15 morphologic parameters and 12 chromatic parameters of cervical cells,700 cervical cells were classified as normal cells,low-grade squamous intraepithelial lesion(LSIL) cells,high-grade squamous intraepithelial lesion(HSIL) cells,and cervical cancer cells.STATISTICA 7.0 was used to establish and train the neural network model,and VC++.NET was used to call the model.Results:The goodness of fit of the neural network model in training set was 97.3%,and the classification accuracy in testing set was 95.4%.In testing set,the recognition rate was 96% in normal cells,94% in LSIL cells,100% in HSIL cells,and 88% in cervical cancer cells.The sensitivity order of input parameters in the RBF artificial neural network was approximately consistent with that of characteristics of cell pathology.Conclusion:Cervical cancer cells,especially HSIL cells,can be well recognized by RBF artificial neural networks.RBF neural network can be widely applied in computer aided diagnosis.