基于改进CNN的宫颈细胞自动分类算法

本文采用深度学习算法中的卷积神经网络对细胞图像进行识别, 实现对宫颈细胞图像的自动分类. 首先对宫颈细胞进行预处理, 通过细胞核裁剪解决图像输入尺寸不一的问题, 对图像进行翻转平移, 对数据集进行扩充, 并解决样本量不均衡的问题; 接着选取VGG-16网络进行改进, 使用改进后的VGG-16网络进行特征提取, 以及细胞分类; 并采用迁移学习的方法加载预训练网络参数, 进而加快参数收敛速度, 提高分类准确率; 最终通过对网络的训练, 得到了较好的分类结果, 将分类结果与人工提取特征设计分类器的方法相比, 分类的准确率有所提高, 二分类的准确率达97.3%, 七分类的准确率达89%. 实验结果表明: 卷积神经网络对宫颈细胞图像进行自动分类, 分类准确率相比较人工提取特征分类器效果较好, 且分类结果不受分割图像准确率的影响.

Automatic Classification Algorithm of Cervical Cells Based on Improved CNN

In this study, the convolutional neural network under the deep learning framework is applied to the field of cervical cell identification to achieve automatic classification of cervical cell images. Firstly, the cervical cells are pretreated, and the problem of different image input sizes is solved by nuclear cutting, the image is flipped and translated, the data set is expanded, and the sample size imbalance is solved. Then the VGG-16 network is selected for improvement. The improved VGG-16 network is used for feature extraction and cell classification. The migration learning method is used for network pre-training, which speeds up the network convergence speed and improves the classification accuracy. Finally, through the training of the network, it achieves better result. According to the classification results, the classification accuracy is improved compared with the manual extraction feature design classifier. The accuracy of two categories classification is 97.3%, and the accuracy of the seven categories classification is 89%. The experimental results show that the convolutional neural network automatically classifies the cervical cell images, and the classification accuracy is better than that of the artificial extraction feature classifier, and the classification results are not affected by the segmentation image accuracy.