## 2 Related Works  
Compared with deep learning, the traditional machine learning segmentation method is often cumbersome to implement, and doctors need to intervene manually according to their own prior knowledge and professional practical experience in the early feature selection process, which may lead to certain errors in doctors' judgment due to some external factors. As a learning method based on pixel classification, the deep learning model no longer needs doctors to design features manually. The network can actively learn image-related features by selecting reasonable Loss functions and gradient descent algorithms through supervised learning. The method based on deep learning makes the segmentation results obtained by the network more objective and referential because there is not too much manual intervention. Nowadays, deep learning networks have been widely used in medical image processing, among which convolution neural networks are the representative ones, which can be divided into two categories: traditional convolution neural networks and full convolution neural networks. A traditional convolution neural network structure divides the input image into several image blocks and then predicts each image block through the model, whether it is inside or outside the target. [PERSON] et al. [14] combine sparse coding, support vector machine and convolution neural network to realize accurate recognition of melanoma. However, traditional CNN has a fixed requirement for the input size of the network. Different data sets need to be preprocessed and post-processed, and a large number of parameters will be generated in the training process of the network, which occupies a large memory space. The full convolution neural network uses the convolution layer instead of the last full connection layer so that the network can accept any size input and reduce the complexity of the network. In 2015, [PERSON] et al. [15] first proposed a pixel semantic segmentation technology that allows arbitrary input size, called Fully Network (FCN). In the same year, [PERSON] et al. [16] proposed SegNet. Both of them are encoder-decoder segmentation methods. The encoder generates a low-resolution feature map and the decoder samples it to restore it. Finally, the Softmax classifier is used to predict the segmentation area. In 2015, [PERSON] et al. [17] put forward Deeplab and then put forward the last three versions one after another. In view of the neglect of small objects in convolution networks and the multi-scale problems, the Deeplab series successively replaced different pre-training models, introduced extended convolution, hole convolution and ASPP layers, and finally made organic combinations. [PERSON] et al. [18] proposed a new scroll integral cut method called U-Net in 2015, which is a modification and extension of the FCN structure. The whole network has a U-shaped symmetric structure, with an encoder on the left side, including four convolution layers, and a decoder on the right side, including four upsampling layers corresponding to the encoder. The feature map of each convolution layer will pass through the skip connection layer. Nowadays, the U-Net structure has been widely used in medical image lesion segmentation tasks. In recent years, the deep learning method has been applied to the field of skin lesion segmentation. In 2017, the improvement of the U-Net variant and U-Net encoder using pre-training appeared frequently. [PERSON] et al. [19] give a comparison of two skin lesion segmentation methods based on a U-Net histogram equalization and C-means clustering. [PERSON] et al. [20] used the ISBI2017 data set to propose a skin lesion segmentation method using different color spaces of dermatoscope images as training depth convolution-deconvolution neural network (CDNN). [PERSON] et al. [21] made an integrated system combining traditional machine learning methods with deep learning methods. [PERSON] et al. [22] proposed an Attention Gate (AG), which integrates an attention mechanism into U-Net.