## 4 Results
### Comparative Experiment
#### 4.4.2 Comparison on the ISIC2018 Data Set  
This section describes in detail the comparative experiments on the ISIC2018 data set to test the reliability and effectiveness of the proposed network. Table 6 shows the performance index of this network compared with the other four networks, and the best results are indicated in bold. It can be seen from the table that the MSREA-Net model has the best performance in three of the five evaluation indexes, which shows that our algorithm has better performance in skin disease segmentation and can segment the lesion area more accurately. The MSREA-Net model is the best in Acc, Spe and Dice, which are 0.9616, 0.9729 and 0.9284, respectively. Compared with the U-Net network, the MSREA-Net model is 0.0172, 0.039 and 0.042 higher than the U-Net network, respectively. Acc accuracy indicates 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 coefficient of the algorithm proposed in this paper reaches 0.9248, which shows that the segmentation results of this network are more similar to the tag results. Figure 9(a)-f shows the segmentation results of skin lesion images on ISIC2018 data set by different networks. Compared with other networks, our algorithm is more accurate in segmenting images. In Figure 9(a),b,d, the color of the skin injury area is very similar to that of normal skin, and there is no clear dividing line. For this situation, our algorithm can better segment the lesion area, and the edge contour is clear. However, U-Net and CE-Net networks can not capture the boundary information completely, which leads to a poor segmentation effect and unclear edge segmentation. Figure 9(e),f has a high color contrast with normal skin, and the boundary between edge and contour is obvious. Our algorithm in this paper can extract and process edge information well and segment the lesion area correctly. In Figure 9(c), there are some dark lesion areas in the skin lesion area. By comparing and analyzing the segmentation results, it can be seen that U-Net, CE-Net and CPF-Net all have lesion areas missing, and there are many dark lesion areas missing, which shows that the above networks have insufficient image feature extraction and are easy to cause information loss. The MS-RFB module in our algorithm can enrich the image features, capture the global context information, and make the segmentation results more accurate. The comparison results show that our algorithm can better extract \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Acc** & **Sen** & **Spe** & **Dice** & **Jac** \\ \hline U-Net & 0.9444 & 0.8859 & 0.9339 & 0.8864 & 0.7724 \\ CE-Net & 0.9593 & 0.8747 & 0.9701 & 0.8959 & 0.8162 \\ DeepLabv3+ & 0.9587 & **0.8974** & 0.9637 & 0.8926 & 0.8143 \\ CPF-Net & 0.9602 & 0.8344 & 0.9617 & 0.8963 & **0.8210** \\ Ours & **0.9616** & 0.8942 & **0.9729** & **0.9284** & 0.8148 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Different network results on ISIC2018 data set, bold denotes the best result. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Network** & **Acc** & **Sen** & **Spe** & **Dice** & **Jac** \\ \hline
[49] & AttU-net & 0.9145 & 0.7998 & 0.9776 & 0.8082 & 0.7173 \\
[50] & MB-DCNN & 0.9311 & 0.8325 & 0.9684 & 0.8427 & 0.7603 \\
[51] & FAT-Net & 0.9326 & 0.8392 & 0.9725 & 0.8500 & 0.7653 \\
[52] & DAGAN & 0.9304 & 0.8363 & 0.9726 & 0.8425 & 0.7594 \\
[53] & CMM-Net & 0.9393 & 0.8769 & 0.9623 & 0.8578 & 0.7765 \\ Ours & MSREA-Net & 0.9389 & 0.8354 & 0.9438 & 0.8867 & 0.7925 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Objective comparison of different networks on ISBI2017 data set.