Residual neural network (ResNet) has witnessed tremendous amount of attention in deep learning research over the last few years and has made great achievements in computer vision. In this paper

the ResNet is summarized in the following aspects: Firstly

the basic structure and working principle of the ResNet are expounded; Secondly

in model development

the eight network models of the ResNet are summarized in time sequence; Thirdly

in structural optimization

the research progress is described from five aspects of ResNet

including convolutional layer

pooling layer

residual unit

fully connected layer and the whole network; Finally

the application of ResNet in medical images processing is mainly discussed from two aspects of image recognition and image segmentation. In this paper

the principles

models

and structures of ResNet are systematically summarized

which has positive significance to the research and development of ResNet.