Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image
Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image
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DOI：
10.1007/s10916-019-1457-4
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发表时间：
2019-11-01
影响因子：
5.3
通讯作者：
Lu, Xinhong
中科院分区：
文献类型：
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作者：
Zhang, Xiaohong;Jiang, Liqing;Lu, Xinhong
尿沉渣识别在计算机视觉领域引起了越来越多的关注。针对自然状态下多视点细胞灰度变化和细胞信息丢失等问题，提出了一种基于多视点深度残差学习的多视点尿液细胞识别方法。首先在残差网络的基础上设计卷积网络，从不同角度提取尿沉渣特征，并引入深度可分离卷积来降低网络参数。其次，嵌入压缩激励块学习特征权值，利用特征重校准提高网络表示能力，并通过加入空间金字塔池增强网络的鲁棒性。最后，为了进一步优化识别结果，采用了带权重衰减的ADAM优化方法来加速网络模型的收敛。在自建的尿液显微图像数据集上的实验表明，该方法具有最高的分类精度，并减少了网络计算时间。
Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-view urine cell recognition method based on multi-view deep residual learning is proposed to solve some existing problems, such as multi-view cell gray change and cell information loss in the natural state. Firstly, the convolutional network is designed to extract the urine sediment features from different perspectives based on the residual network, and the depth-wise separable convolution is introduced to reduce the network parameters. Secondly, Squeeze-and-Excitation block is embedded to learn feature weights, using feature re-calibration to improve network representation, and the robustness of the network is enhanced by adding spatial pyramid pooling. Finally, for further optimizing the recognition results, the Adam with weight decay optimization method is used to accelerate the convergence of the network model. Experiments on self-built urine microscopic image data-set show that our proposed method has state-of-the-art classification accuracy and reduces network computing time.