| Sumario: | 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.
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