## 4 Experiment and Results
### Ablation Research  
To verify the effectiveness and necessity of each component in the proposed convolutional modulation network--particularly the contributions of the multi-scale hybrid attention module and the key designs within the long short-term memory (LSTM) mechanism--we conduct ablation studies from two perspectives: network architecture design and detailed component analysis. For fair comparison and evaluation, a baseline model is constructed by retaining only the local feature extraction and feature mapping parts of the DenseUNet module. Based on this baseline, we incrementally incorporate the proposed modules and perform experiments on the ISIC2017 dataset to systematically assess the  
Figure 16: Performance comparison of different segmentation algorithms on the ISIC2018 dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Acc (\%)** & **Dice (\%)** & **Sp (\%)** & **Sen (\%)** & **mIoU (\%)** \\ \hline U-net [45] & 92.51 \(\pm\) 0.86 & 86.61 \(\pm\) 0.64 & 94.23 \(\pm\) 0.96 & 85.24 \(\pm\) 0.76 & 80.17 \(\pm\) 0.55 \\ UNet++ [54] & 93.72 \(\pm\) 0.65 & 87.34 \(\pm\) 0.45 & 94.52 \(\pm\) 0.51 & 88.76 \(\pm\) 0.46 & 81.64 \(\pm\) 0.42 \\ FAT-Net [48] & 94.71 \(\pm\) 0.54 & 89.09 \(\pm\) 0.39 & 95.06 \(\pm\) 0.46 & 91.01 \(\pm\) 0.38 & 82.02 \(\pm\) 0.37 \\ Swin-Unet [52] & 95.12 \(\pm\) 0.36 & 87.94 \(\pm\) 0.45 & 96.16 \(\pm\) 0.35 & 89.26 \(\pm\) 0.42 & 82.41 \(\pm\) 0.26 \\ LCAUnet [50] & 95.46 \(\pm\) 0.28 & 91.93 \(\pm\) 0.32 & 96.11 \(\pm\) 0.29 & 91.34 \(\pm\) 0.3 & 81.59 \(\pm\) 0.35 \\ SCSONet [51] & 94.58 \(\pm\) 0.37 & 89.51 \(\pm\) 0.31 & 95.86 \(\pm\) 0.19 & 9  
impact of each component on overall network performance. The experimental results are presented in Table 6.  
As shown in Figure 17, the components of the proposed network substantially contribute to overall performance enhancement. Beginning with the baseline model, which achieves an accuracy of 92.21%, a Dice coefficient of 81.82%, and a mean Intersection over Union (mIoU) of 80.93%, consistent improvements are observed as each module is progressively incorporated. As shown in Figure 18, the integration of the attention mechanism results in a marked increase in segmentation accuracy, with the Dice coefficient rising to 84.32% and mIoU to 82.71%, highlighting its effectiveness in capturing spatial dependencies. Further enhancement is achieved by incorporating the Convolutional Block Attention Module (CBAM), which elevates the Dice coefficient to 86.76% and mIoU to 83.63%, demonstrating the advantages of combined channel and spatial attention in refining feature representations. The introduction of the Long Short-Term Memory (LSTM) module significantly augments the model's capability to capture temporal and contextual information, leading to a Dice coefficient of 89.92% and mIoU of 85.05%. Ultimately, the complete model achieves the highest performance, with an accuracy of 95.07%, a Dice coefficient of 90.25%, and mIoU of 86.59%, thereby validating the complementary contributions and necessity of each architectural component.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Acc (\%)** & **Dice (\%)** & **mIoU (\%)** \\ \hline Baseline & 92.21 \(\pm\) 0.31 & 81.82 \(\pm\) 0.22 & 76.93 \(\pm\) 0.37 \\ Base + Att & 92.78 \(\pm\) 0.35 & 84.32 \(\pm\) 0.27 & 78.71 \(\pm\) 0.32 \\ Base + Att + CBAM + BDC-LSTM & 93.42 \(\pm\) 0.22 & 86.76 \(\pm\) 0.19 & 81.63 \(\pm\) 0.28 \\ Ours & 94.63 \(\pm\) 0.31 & 89.92 \(\pm\) 0.27 & 82.05 \(\pm\) 0.24 \\ Ours & 95.07 \(\pm\) 0.16 & 90.25 \(\pm\) 0.14 & 83.59 \(\pm\) 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of the overall network on the ISIC2017 dataset.  
Figure 17: Comparative performance analysis of ablation studies on the overall network architecture.