Abstract: To solve the problems of low accuracy and poor real-time property for cervical cell classification, this paper proposes an improved coordinate attention (ICA) module, and designs a lightweight deep convolution neural network ICA-Res2Net by combining with the new residual structure Res2Net and spatial pyramid pooling layer. Firstly, cross-convolution between feature sub-blocks of Res2Net network is adopted to extract finer granularity information in feature layer. Then, the spatial pyramid pooling is used to extract local regional features, thus the features can be effectively extracted without increasing the number of training parameters. The improved lightweight attention module is further introduced to weight each pixel in the feature layer through operations such as horizontal pooling and vertical pooling, so as to strengthen the important detailed features and help the network locating the objects of interest. In addition, in order to effectively prevent the degradation of deep network, the proposed ICA-Res2Net network retains the design of skip connection in residual network; and the network parameters are trained by combined the Softmax loss function with the center loss function to improve the classification accuracy. Applying the lightweight network proposed in this paper to classify cervical cell images in the SIPaKMeD public dataset, the test classification accuracy can reach 98.65%, and the training parameters of the network are much fewer than those of the classic networks such as ResNet50 and DenseNet121, which significantly improves the classification efficiency of cervical cell images.

Key words: cervical cell, deep convolutional neural network, image classification, multi-scale characteristics, attention mechanism

摘要： 针对目前宫颈细胞分类准确率不高以及实时性差的问题，提出改进的协调注意力模块（improved coordinate attention，ICA），并结合新型残差结构Res2Net以及空间金字塔池化层，设计一种轻量级深度卷积神经网络ICA-Res2Net。利用Res2Net网络特征子块间的交叉卷积，提取特征层中更细粒度的信息；采用空间金字塔池化提取局部区域特征，从而在不增加训练参数量的同时有效提取特征；进一步引入改进的轻量级注意力模块，通过横向池化、纵向池化等操作，给予特征层各像素点不同的加权值，强化重要细节特征，帮助网络定位感兴趣对象。此外，为有效防止深度网络的退化，提出的ICA-Res2Net网络保留了残差网络的跳跃连接设计；并联合Softmax损失函数和中心损失函数对网络参数进行训练，提高其分类准确率。利用提出的轻量级网络对SIPaKMeD公开数据集中的宫颈细胞图像进行分类，测试集的分类准确率达到98.65%，且网络的训练参数比ResNet50、DenseNet121等经典网络更少，显著提升宫颈细胞图像的分类效率。

关键词: 宫颈细胞, 深度卷积神经网络, 图像分类, 多尺度特征, 注意力机制