Application of deep learning in image recognition and diagnosis of gastric cancer

Table 4 Comparison of convolutional neural network, full convolutional neural network, and generative adversarial network models

Model features	Contributions	Advantages	Disadvantages	Scope of application
CNN	The topology can be extracted from a two-dimensional image, and the backpropagation algorithm is used to optimize the network structure and solve the unknown parameters in the network	Shared convolution kernel, processing high-dimensional data without pressure; Feature extraction can be done automatically	When the network layer is too deep, the parameters near the input layer will be changed slowly by using BP propagation to modify parameters. A gradient descent algorithm is used to make the training results converge to the local minimum rather than the global minimum. The pooling layer will lose a lot of valuable information	Suitable for data scenarios with similar network structures
FCN	The end-to-end convolutional network is extended to semantic segmentation. The deconvolution layer is used for up-sampling; A skip connection is proposed to improve the roughness of the upper sampling	Can accept any size; Input image; Jump junction; The structure combines fine layers and coarse; Rough layers, generating precise segmentation	The receptive field is too small to obtain the global information;Small storage overhead	Applicable to large sample data
GAN	With adversarial learning criteria, there are two No's: The same network, not a single network	Can produce a clearer, more realistic sample; any generated network can be trained	Training is unstable and difficult to train; GAN is not suitable for processing data in discrete form	Suitable for data generation (e.g., there are not many data sets with labels), image style transfer; Image denoising and restoration; Used to counter attacks

CNN: Convolutional neural network; FCN: Full convolutional neural network; GAN: Generative adversarial network.