## 4 Experiment and Results
### Comparative Experiments  
#### 4.1.1 Comparative Experiments on PH\({}^{2}\)  
To validate that the proposed model outperforms traditional convolutional neural networks and existing segmentation models, a comprehensive comparison and evaluation were conducted against several state-of-the-art approaches. The quantitative results on the PH\({}^{2}\) dataset are presented in Table 3.  
The experimental results on the PH\({}^{2}\) dataset are summarized in Table 3, demonstrating the performance of various network models across multiple evaluation metrics, including accuracy and Dice coefficient. Figure 9 demonstrates that the proposed method consistently achieves superior results compared to the baseline and current state-of-the-art models, with particularly notable improvements in the Dice coefficient and mean Intersection over Union (mIoU). Specifically, the proposed MA-DenseUNet achieves an accuracy improvement of approximately 4.95% over the baseline U-Net and about 0.3% over the SCSONet model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Acc (\%)** & **Dice (\%)** & **Sp (\%)** & **Sen (\%)** & **mIoU (\%)** \\ \hline U-net [45] & 91.32 \(\pm\) 0.43 & 89.45 \(\pm\) 0.31 & 95.63 \(\pm\) 0.27 & 91.32 \(\pm\) 0.56 & 84.12 \(\pm\) 0.39 \\ Att-Unet [46] & 92.13 \(\pm\) 0.24 & 90.06 \(\pm\) 0.26 & 96.42 \(\pm\) 0.18 & 92.13 \(\pm\) 0.19 & 85.83 \(\pm\) 0.24 \\ DSNet [47] & 94.80 \(\pm\) 0.18 & 92.02 \(\pm\) 0.35 & 96.15 \(\pm\) 0.29 & 92.46 \(\pm\) 0.33 & 87.26 \(\pm\) 0.39 \\ FAT-Net [48] & 97.01 \(\pm\) 0.25 & 94.48 \(\pm\) 0.27 & 97.44 \(\pm\) 0.19 & 94.41 \(\pm\) 0.27 & 87.61 \(\pm\) 0.16 \\ APT-Net [49] & 96.16 \(\pm\) 0.36 & 94.66 \(\pm\) 0.42 & 97.93 \(\pm\) 0.34 & 94.05 \(\pm\) 0.39 & 86.96 \(\pm\) 0.22 \\ LCAUnet [50] & 97.32 \(\pm\) 0.15 & 95.24 \(\pm\) 0.18 & 98.24 \(\pm\) 0.16 & 95.43 \(\pm\) 0.14 & 88.02 \(\pm\) 0.23 \\ SCSONet [51] & 95.97 \(\pm\) 0.29 & 95.62 \(\pm\) 0.22 & 98.03 \(\pm\) 0.18 & 96.22 \(\pm\) 0.23 & 89.04 \(\pm\) 0.14 \\ Ours & 96.28 \(\pm\) 0.21 & 95.85 \(\pm\) 0.13 & 98.67 \(\pm\) 0.16 & 96.94 \(\pm\) 0.24 & 90.39 \(\pm\) 0.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of different segmentation algorithms on the PH\({}^{2}\) dataset. Although its accuracy is marginally lower than that of LCAUnet, the proposed method yields substantial gains in Dice coefficient, outperforming U-Net by approximately 6.4%, and surpassing LCAUnet and SCSONet by about 0.6% and 0.2%, respectively. In terms of mIoU, MA-DenseUNet exhibits an improvement of approximately 7.2% compared to U-Net and about 1.3% relative to SCSONet. Moreover, as shown in Figure 10, the proposed model consistently outperforms other comparative methods across all additional evaluation criteria. These results clearly demonstrate that the MA-DenseUNet is capable of accurately delineating lesion regions from the background, thereby significantly enhancing the segmentation performance in terms of both accuracy and robustness. The specific segmentation results are shown in Figure 11.  
Figure 10: Comparison results on the PH\({}^{2}\) dataset. (**a**) Segmentation result from U-Net; (**b**) segmentation result from LCAUnet; (**c**) segmentation result from SCSONet; (**d**) segmentation result from MA-DenseUNet. Figure 9: Performance comparison of different segmentation algorithms on the PH\({}^{2}\) dataset. To achieve a more comprehensive and precise evaluation of the segmentation performance across different models, we further conducted an in-depth analysis of the outputs generated by several state-of-the-art networks, as illustrated in Figure 11. For skin lesion images characterized by large background areas and relatively small lesion regions, U-Net and other mainstream models are generally capable of accurately segmenting the principal lesion regions; however, significant discrepancies in boundary delineation remain when compared with the ground truth (as shown in Figure 11a,b).