## 4 Experiment and Results
### Comparative Experiments
#### 4.1.2 Comparative Experiments on ISIC2017  
On the ISIC2017 dataset, the proposed method consistently outperforms the baseline U-Net and several state-of-the-art segmentation networks across a range of performance metrics. The experimental results, as summarized in Table 4, provide a comparative analysis of different models based on accuracy, Dice coefficient, mean Intersection over Union (mIoU), and other key evaluation metrics. Specifically, as shown in Figure 12, the proposed method demonstrates an accuracy improvement of approximately 3.5% over the baseline U-Net, and an enhancement of about 0.5% over the current SCSONet. In terms of the Dice coefficient, the proposed model outperforms U-Net by approximately 11.9%, and surpasses SCSONet and TESL-Net by about 1.4% and 0.16%, respectively. For the mIoU metric, the proposed approach shows a notable improvement of approximately 7.0% compared to U-Net, and achieves gains of about 1.3% and 1.27% over SCSONet and EGE-Unet, respectively. Figure 11: Visual comparison between ground truth and prediction. (**a**) Segmentation result from U-Net; (**b**) segmentation result from LCAUnet; (**c**) segmentation result from SCSONet; (**d**) segmentation result from MA-DenseUNet. Red contours represent ground truth boundaries, while green contours indicate predicted segmentation boundaries. Furthermore, the MA-DenseUNet model excels in other critical performance metrics, including sensitivity and specificity, outperforming most of the comparative methods. These results provide compelling evidence of its superior segmentation performance. The qualitative differences in segmentation results across various models are illustrated in Figure 13. In summary, the proposed MA-DenseUNet model achieves robust and accurate segmentation across lesions of varying sizes and shapes, particularly excelling at capturing fine-grained lesion boundaries and textural details. This capability significantly enhances the overall accuracy and robustness of lesion segmentation, demonstrating the method's effectiveness in practical applications. The specific segmentation results are shown in Figure 14. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Acc (\%)** & **Dice (\%)** & **Sp (\%)** & **Sen (\%)** & **mIoU (\%)** \\ \hline U-net [45] & 91.51 \(\pm\) 0.52 & 78.31 \(\pm\) 0.48 & 95.44 \(\pm\) 0.39 & 80.65 \(\pm\) 0.68 & 76.58 \(\pm\) 0.52 \\ Att-U-net [46] & 92.65 \(\pm\) 0.43 & 80.82 \(\pm\) 0.23 & 97.86 \(\pm\) 0.36 & 80.03 \(\pm\) 0.27 & 78.74 \(\pm\) 0.31 \\ FAT-Net [47] & 93.36 \(\pm\) 0.26 & 85.06 \(\pm\) 0.32 & 97.32 \(\pm\) 0.19 & 84.01 \(\pm\) 0.23 & 76.51 \(\pm\) 0.19 \\ Swin-Unet [52] & 94.76 \(\pm\) 0.38 & 81.94 \(\pm\) 0.56 & 96.18 \(\pm\) 0.42 & 88.06 \(\pm\) 0.25 & 80.93 \(\pm\) 0.36 \\ LCAUnet [50] & 94.04 \(\pm\) 0.29 & 86.63 \(\pm\) 0.32 & 96.53 \(\pm\) 0.21 & 85.24 \(\pm\) 0.41 & 76.14 \(\pm\) 0.38 \\ SCSONet [51] & 94.52 \(\pm\) 0.32 & 88.97 \(\pm\) 0.28 & 97.07 \(\pm\) 0.19 & 84.02 \(\pm\) 0.35 & 82.26 \(\pm\) 0.33 \\ EGE-Unet [53] & 94.61 \(\pm\) 0.22 & 88.83 \(\pm\) 0.16 & 96.98 \(\pm\) 0.23 & 86.26 \(\pm\) 0.29 & 82.32 \(\pm\) 0.28 \\ TESL-Net [34] & 95.80 \(\pm\) 0.29 & 90.09 \(\pm\) 0.31 & 97.29 \(\pm\) 0.36 & 91.10 \(\pm\) 0.26 & 83.37 \(\pm\) 0.35 \\ Ours & 95.07 \(\pm\) 0.32 & 90.25 \(\pm\) 0.2 & 97.46 \(\pm\) 0.17 & 91.82 \(\pm\) 0.16 & 83.59 \(\pm\) 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of different segmentation algorithms on the ISIC2017 dataset. Figure 12: Performance comparison of different segmentation algorithms on the ISIC2017 dataset. We have provided a detailed overview of the performance of different state-of-the-art algorithms on the ISIC2017 dataset. To further assess the segmentation performance of  
Figure 14: Visual comparison between ground truth and prediction. (**a**) Segmentation result from U-Net; (**b**) segmentation result from LCAUnet; (**c**) segmentation result from EGE-Unet; (**d**) segmentation result from MA-DenseUNet.