Classification of Cervical Cells Based on Convolution Neural Network

To achieve accurate and rapid detection for abnormal cervical cells in computer-assisted cytology test,an automatic classification method based on convolution neural network is proposed.First,the classification network was initialized with the pre-trained network structure and parameters,and cervical cell images were imported into it in batches.Then the output data is normalized to the probability of each label by Softmax,and cross-entropy is set as the loss function.The network structure was improved by batch normalization,and parameters were optimized by back propagation to minimize the loss function.Eventually,the optimal network was selected.The five-fold cross validation shows specificity,H-mean and F-measure are improved by 19.46%,10.71%and 5.09%respectively in contrast with benchmark on Herlev dataset.