## 4 Results
### Comparative Experiment  
#### 4.4.1 Comparison on the ISBI2017 Data Set  
In order to verify the reliability and effectiveness of the proposed network for skin lesion image segmentation, we select U-Net, a semantic image segmentation encoder based on separable convolution (DeepLabv3+) [46], a context encoder network for two-dimensional medical image segmentation (CE-Net) [47], and a context pyramid fusion network for medical image segmentation (CPF-Net) [48] to compare the indexes with the proposed network. The U-Net network structure is symmetrical. The left and right codecs are symmetrical and connected by a bottleneck layer. The encoder extracts image features by downsampling and the decoder reconstructs the feature map to restore the target size and obtain the segmentation result. Based on the U-shaped network, CE-Net proposes a context extraction module composed of dense hole convolution and residual multi-core pool blocks, which are applied to different two-dimensional medical image segmentation tasks. DeepLabv3+ combines the advantages of encoding and decoding structure and spatial pyramid pooling structure, expands the decoding module of DeepLabv3, and improves the processing ability of model boundary information. CPF-Net combines a global pyramid guidance module and a scale-aware pyramid module to fuse global context information. The above five networks are tested in the same experimental environment; the comparison results are shown in Table 4 and the optimal index is indicated in bold. Table 4 gives the performance index of this network and the other four comparison networks on the ISBI2017 data set. A comprehensive comparison shows that our algorithm has better performance. Among the five networks, our algorithm accuracy of this network is the highest, which is 93.89%. The accuracy represents the ratio of correctly estimated samples to the total number of samples, which shows that this network has the best performance for skin disease image segmentation. The Dice similarity coefficient is used to evaluate the coincidence between the segmented image and the tag image. The Dice similarity coefficients of the proposed algorithm reach 88.67%, which shows that the segmentation results of this network are more similar to the tag results. Our algorithm excels in all other metrics except for slightly lower Spe than DeepLabv3+. Figure 9 shows the segmentation results of skin lesion images on the ISBI2017 data set by different networks. From left to right, the images are Image, mask, U-Net segmentation \begin{table}
\begin{tabular}{c c c c c c} \hline \hline Network & Acc & Sen & Spe & Dice & Jac \\ \hline U-Net & 0.9236 & 0.7528 & 0.9221 & 0.8211 & 0.7065 \\ CE-Net & 0.9351 & 0.7653 & 0.9317 & 0.8561 & 0.7754 \\ DeepLabv3+ & 0.9344 & 0.7859 & **0.9447** & 0.8587 & 0.7796 \\ CPF-Net & 0.9362 & 0.8166 & 0.9411 & 0.8621 & 0.7816 \\ Ours & **0.9389** & **0.8354** & 0.9438 & **0.8867** & **0.7925** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different network results on ISBI2017 data set, bold denotes the best result. results, CE-Net segmentation results, DeepLabv3+ segmentation results, CPF-Net segmentation results and the segmentation results of our algorithm. Comprehensive analysis shows that this network performs better than other networks. In Figure 9a,d,e, there are a large number of hairs and U-Net and CE-Net are affected by hairs, so the segmentation of the lesion area is not accurate enough, and the contour segmentation is poor. For Figure 9b,c, which have similar lesion regions and backgrounds, the proposed network can clearly segment their contours, better deal with the edge information, reduce the influence of hair on the segmentation effect, and have stronger robustness. In general, our method in the segmentation of various skin lesions, because of other methods, can better deal with the occlusion of hair and blood vessels and can better segment the lesion area for doctors to provide a reference for diagnosis.