Abstract: Detection of antinuclear antibodies by human epithelial cells (HEp-2) is a common method for the diagnosis of autoimmune diseases. Image recognition of HEp-2 cells is of a great significance for the diagnosis and treatment of many autoimmune diseases. Aiming at the problems of low efficiency and high labor intensity caused by manual evaluation methods, a HEp-2 cell image classification model based on the depth residual shrinkage network is proposed. The model is improved on the basis of the deep residual network, and the residual learning module can train the deeper network by using the identity mapping method. In each residual learning module, a soft threshold nonlinear transformation sub network is embedded. The soft threshold is used to eliminate the noise and redundant information in the data. These thresholds are automatically learned by the sub network. Experiments show that this method has good performance and is superior to other depth neural network methods.

Key words: deep residual shrinkage network, soft threshold, convolutional neural network, image recognition