# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Comparative Experiments  
#### 4.4.1 Comparison on the ISIC2018 Dataset  
To validate the effectiveness of our proposed model, we employed the REDAUNet model on the ISIC2018 dataset and compared it with several other advanced semantic segmentation models, including UNet, Atten UNet, and UNeXt. For each model, we  
Figure 8: Training and validation processes on the ISIC-2018 dataset: (**a**) curves of _mIoU_ metric; (**b**) curves of _Dice_ metric. Figure 9: Ablation experiment results of model improvement. (**a**) Segmentation result of UNet3+; (**b**) segmentation result of UNet3+ with cross-residual; (**c**) segmentation result of UNet3+ with cross-residual and ECA; (**d**) segmentation result of REDAUNet. extensively tested its segmentation performance, and the results are presented in Table 3. Additionally, Figure 10 showcased the segmentation effects more intuitively for image comparison. Based on the results from the ISIC2018 dataset, our proposed REDAUNet model exhibited superior segmentation performance on most metrics compared to the other models, especially in the _Dice_ coefficient and _mloU_ metric, which are significantly higher than mainstream network performance. Furthermore, the _Dice_ coefficient and _mloU_ metric are 1.75% and 2.44% higher than that of the current best UNeXt model. The Acc and Spec metrics reached 0.9444 and 0.9774, respectively, which improved by 0.07% and 0.91% compared to the second-best segmentation result, achieving excellent results. Although the score of the _Sens_ metric is slightly lower than another network structure (UNet3+), it still achieved a good second-best score of 0.9087, which is 1.64% lower than the UNet3+ result. In summary, our REDAUNet model demonstrated more prominent segmentation results on the ISIC2018 dataset, proving the practical application prospects of this method in skin lesion image segmentation. To more accurately evaluate the performance of each model, we conducted a detailed analysis of the output from the selected models. The specific results are shown in Figure 10. For images with clear edges, the similarity of the results of each model to the manually annotated contours is relatively high. However, when the lesion area is close in color to the surrounding skin, the segmentation results of UNet3+, UNeXt, and the proposed model are nearer to the ground truth image, while UNet and Atten UNet models have more areas of  
Figure 10: Comparison results on the ISIC2018 dataset. (**a**) Segmentation result from UNet; (**b**) segmentation result from UNet3+; (**c**) segmentation result from Atten UNet; (**d**) segmentation result from UNeXt; (**e**) segmentation result from REDAUNet. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & _Acc_ & _Dice_ & _Spec_ & _Sens_ & _mIoU_ \\ \hline UNet [9] & 0.9301 & 0.8742 & 0.9094 & 0.8586 & 0.7786 \\ UNet3+ [11] & 0.9297 & 0.8819 & 0.9341 & 0.9251 & 0.8055 \\ Atten UNet [12] & 0.9425 & 0.8762 & 0.9605 & 0.8721 & 0.7915 \\ UNeXt [38] & 0.9437 & 0.8845 & 0.9683 & 0.8726 & 0.8079 \\ REDAUNet & 0.9444 & 0.9020 & 0.9774 & 0.9087 & 0.8323 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative experimental results on the ISIC2018 dataset. incorrect segmentation, exhibiting obvious differences from the real image, with prominent problems of excessive segmentation or insufficient segmentation, as shown in Figure 10a,c. Although compared to the above two models, UNet3+ and UNeXt perform better, UNet3+ lacks smooth edges for edge-blurred image segmentation results, and UNeXt's handling of irregular edge details is not as good as that of the REDAUNet model. Figure 10e shows that the suggested model can adequately capture the detailed features of the lesion edge, reduce the misclassification of noise pixels, and yield segmentation results that are more similar to the ground truth for skin lesion areas and complex lesion edge colors similar to the ground truth.