基于误差反向传播神经网络的胃癌细胞识别研究

目的探讨误差反向传播(backpro pagation,BP)神经网络在胃癌细胞识别中的应用价值。方法在308例胃切除病例的胃组织切片中选取510个胃细胞,其中腺癌细胞210个,非癌性细胞300个,测量细胞的10个形态学特征。将所得到的数据随机分成A组(训练组)和B组(测试组)。建立三层BP神经网络,并利用A组数据对神经网络进行训练,再利用A、B两组数据对网络模型进行检验测试。结果BP神经网络对A组细胞识别的灵敏度为99%,特异度为99%,阳性预测值为98%,阴性预测值为99%,识别正确率为98%;对B组细胞识别的灵敏度为99%,特异度为97%,阳性预测值为96%,阴性预测值为99%,识别正确率为98%。ROC曲线下面积为0.99。结论本研究结果显示,BP神经网络用于胃癌细胞识别非常有效,可用于胃癌细胞的自动识别。

Research on Recognizing Gastric Cancer Cell Based on Back Propagation Neural Network

Objective To investigate the value of back propagation (BP) neural network for recognizing gastric cancer cell. Methods A total of 510 cells was selected from 308 patients. There were 210 gastric adenocarcinoma cells and 300 non-cancer gastric cells. Ten morphological parameters were measured for each cell. These data were randomly divided into two groups: training dataset (A) and test dataset (B). A three- layer BP neural network was built and trained by using dataset A. The network was then tested with dataset A and B. Results For data A, the sensitivity of network was 99%, specificity 99%,positive predictive value 98%, negative predictive value 99%, and accuracy 99%. For data B, the sensitivity of network was 99%, specificity 97%, positive predictive value 96%, negative predictive value 99%, the accuracy 98%. With receiver operator characteristic (ROC) curve evaluation, the area under ROC curve was 0.99. Conclusion The model based on BP neural network is very effective. A BP neural network can be used for effectively recognizing gastric cancer cell.