## 2 Related Work
### Introduction Classic of Deep Learning Network Models  
[PERSON] et al. proposed a VGG series of deep learning network models (VGG-Net), of which VGG-16 is the most representative [24]. VGG-Net can imitate a larger receptive field by using multiple \(3\times 3\) filters, enhancing nonlinear mapping, reducing parameters, and improving the network to better its analytics. Meanwhile, VGG-16 continues to deepen the previous VGG-Net with 13 convolutional layers and three fully connected layers. With a continuous increase in the convolution kernel and convolution layer, the nonlinear ability of the model becomes stronger. VGG-16 can better learn the features in images and achieve good performance in image classification, segmentation, and detection. [PERSON] proved that as the depth of the network increases, the accuracy of the image analysis increases [24]. Nevertheless, this increase in depth is limited. An excessively increase in the depth of the network may lead to network degradation problems. Therefore, the optimal network depth of the VGG-Net is set to 16-19 layers. Moreover, VGG-16 has three fully connected layers, which causes more memory to be occupied, a long training time, and difficulty in tuning the parameters. [PERSON] et al. proposed a ResNet series of networks and added a residual structure in the networks to solve the problem of network degradation [25]. The ResNet model introduces the jumpy connection method (shortcut connection). This connection method allows the residual structure to skip levels that are not fully trained in the feature extraction process and increases the model's utilization of feature information during the training process. As the most classical model in the ResNet series, ResNet50 has a 50-layer network structure. This model adopts a highway network structure, which allows the network to have strong expression capabilities and acquire more advanced features. Therefore, it is widely used in image analysis. However, the network model is significantly deep and complicated; therefore, determining the layers in the deep network that are not thoroughly trained and then optimizing the network is a complex problem. [PERSON] et al. proposed a GoogLeNet network model, which has the advantage of reducing the complexity of networks based on ResNet. They first proposed Inception-V1, whose network is 22 layers deep and comprises multiple inception structure cascades as basic modules; each inception module consists of a \(1\times 1\), \(3\times 3\), \(5\times 5\) convolution kernel and a \(3\times 3\) maximum pooling, which is similar to the idea of multiscale, and increases the adaptability of the network to different scales [26]. With the continuous improvement of the inception module, the Inception-V2 network uses two \(3\times 3\) convolutions instead of \(5\times 5\) convolutions. This improves the _Batch Normalization_ (BN) method, which rescales the data distribution to accelerate the model convergence [27]. Inception-V3 network introduces the idea of decomposing convolution, splitting a larger two-dimensional convolution into two smaller one-dimensional convolutions, further reducing the number of calculations [28]. Concurrently, Inception-V3 optimizes the inception module, embeds the branch in the branch, and improves the model's accuracy. Xception is another improvement after Inception-V3 [29]. It mainly uses depthwise separable convolution to replace the convolution operation in Inception-V3. The Xception model uses deep separable convolution to increase the width of the network, which improves the accuracy of the classification and ability to learn subtle features. Xception adds a residual mechanism similar to ResNet to significantly improve the speed of convergence during training and accuracy of the model. However, Xception is relatively fragmented during the calculation process and results in a slower iteration speed during training. U-Net is a CNN that was initially used to perform medical image segmentation. The U-Net architecture is symmetrical.