# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Ablation Experiments  
This study aims to explore the contributions of the CR module, ECA attention mechanism, and DenseASPP module to the performance of semantic segmentation networks. A baseline model based on the UNet3+ was established on the ISIC-2018 dataset. We compared the performance improvement effects of different modules with the baseline model. Table 2 summarizes the experimental results. The first comparison is between UNet3+ with residual module and UNet3+. It is observed that UNet3+ with residual module achieves better results compared with UNet3+ in terms of _Dice_, _Spec_, and _mIoU_ metrics. The percentage improvements are increased by 0.89%, 3.13%, and 1.17%, respectively. However, the _Sens_ value of UNet3+ with a residual module is less than that of UNet3+ because the residual module can pay more attention to features with higher relevance. Then, the second comparison is whether UNet3+ with a residual module adds an ECA attention mechanism. The results in Table 2 indicate that UNet3+ with a residual module and ECA attention mechanism achieved slightly better results by highlighting important features and ignoring irrelevant ones than only UNet3+ with a residual module in terms of the five evaluation metrics. Finally, the third comparison is whether the DenseASPP module is introduced to UNet3+ with a residual module and ECA attention mechanism. As the DenseASPP module can cover multiple ASPP modules and adapt to various lesion sizes to further improve the model performance, experimental results indicate that the proposed module with DenseASPP yields better results, and the percentage improvements of _Acc_, _Dice_, _Spec_, _Sens_, and _mIoU_ metrics were 0.31%, 0.45%, 0.51%, 2.06%, and 0.59%, respectively. The results show that skin lesion segmentation performance can be significantly enhanced by improving the way of feature extraction, increasing the attention to relevant features, and acquiring different receptive field features, and the boundary of the segmentation can be made smoother, approaching or even surpassing the ground truth image. For a more intuitive understanding of the segmentation effect of each model in the ablation experiment, Figure 8 displays the _mIoU_ metric curve and _Dice_ coefficient curve of each epoch on the training and validation sets, and Figure 9 shows the segmentation results of four models on the same dataset. Through comparative analysis, we found that the UNet3+ model has significant segmentation errors, particularly with insufficient segmentation performance for detailed parts and even under-segmentation, as shown in Figure 9a. After new residual modules were introduced on the basis of the baseline model, the edge segmentation became smoother, as shown in Figure 9b. Although this mechanism has achieved some improvements, there still exist segmentation deficiencies when the lesion area closely resembles the surrounding area, and the segmentation errors remain substantial compared with the ground truth image. Figure 9c shows that an attention mechanism was added to make the network more focused on lesion-related regions, suppress irrelevant areas, and reduce the probability of erroneous segmentation. To enhance the model's adaptability to lesions of different scales while improving its robustness, this study also includes a DenseASPP module with different expansion rates. Figure 9d illustrates that \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **CR** & **ECA** & **DenseASPP** & _Acc_ & _Dice_ & _Spec_ & _Sens_ & _mIoU_ \\ \hline (a) & - & - & - & 0.9297 & 0.8819 & 0.9341 & 0.9251 & 0.8055 \\ (b) & \(\surd\) & - & - & 0.9389 & 0.8908 & 0.9654 & 0.884 & 0.8172 \\ (c) & \(\surd\) & \(\surd\) & - & 0.9403 & 0.8957 & 0.9723 & 0.8881 & 0.8264 \\ (d) & \(\surd\) & \(\surd\) & \(\surd\) & 0.9444 & 0.9020 & 0.9774 & 0.9087 & 0.8323 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment results on the ISIC2018 dataset.