# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 5 Discussion  
For medical image segmentation tasks, better segmentation results help clinical doctors with important clinical diagnosis and treatment assistance. In this study, we improved and extended the Unet3+ model to propose a semantic segmentation network called REDAUnet. We replaced the simple convolutional stacking method in the original encoder with a newly designed residual module to fully extract shallow and deep features of the images. By using the ECA attention mechanism to adaptively adjust the weights of features on each path, the model can focus more on important feature areas, thus improving the accurate segmentation capability of the target. At the same time, we introduced the DenseASPP module between the encoder and decoder to better handle the boundaries and detailed information of the target, enhancing the ability to handle complex scenes. To achieve effective segmentation of skin lesions, our approach combines image-enhancement techniques to improve the robustness and generalization of the model under different lighting conditions. We conducted detailed experiments and comparative analyses to verify the stability and reliability of our method. In order to verify the effectiveness of each module, we first conducted an ablation experiment on the adopted module. Our Acc, Dice, Spec, and mIoU metrics are 0.9444, 0.9020, 0.9774, and 0.8323, respectively. Compared with previous models, Dice and mIoU metrics of our model increased by 2.01% and 2.68%, indicating that the introduction of modules has significantly improved the performance of the model, as shown in Table 2. We tested our model on two different medical image datasets. First, we compared our model with the other four models under the same environment, as shown in Table 3. Compared with the UNeXt model, our proposed model has a slight improvement in the four indicators, and the improvement in mIoU is 2.44%. In addition, we also compared the advanced networks in the past two years, as shown in Table 5. The proposed model achieved high scores in Spec and mIoU, 0.28% and 0.43% higher than the best model compared, respectively. Experiments on the ISIC2017 dataset show that the proposed model also has good robustness and generalization ability, as shown in Tables 6 and 7. A comprehensive analysis of the segmentation results of REDAUNet on two representative datasets shows that our method can perform well in a variety of complex cases, such as irregular lesion shapes and fuzzy boundaries. However, although the REDAUNet model performs well in semantic segmentation tasks, there are still challenges and room for improvement. First, regarding the issue of a large number of parameters in the Unet3+ model, our model did not reduce the number of parameters. On the contrary, due to the introduction of different modules, there was a slight increase in parameters, requiring higher computational resources. Second, because it contains many \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Model** & _Acc_ & _Dice_ & _Spec_ & _Sens_ & _mIoU_ \\ \hline
[40] & DAGAN & 0.9304 & 0.8425 & 0.9716 & 0.8363 & 0.7594 \\
[41] & FAT-Net & 0.9654 & 0.9109 & 0.9847 & 0.8879 & 0.8364 \\
[44] & MCGU-Net & 0.957 & 0.8927 & 0.9855 & 0.8502 & 0.8062 \\
[45] & SegNet & 0.928 & 0.8339 & 0.9222 & 0.9021 & - \\
[46] & Lesion Net & - & 0.8787 & 0.9608 & 0.8623 & 0.7828 \\
[47] & ResUNet++ & - & 0.8296 & - & - & 80.03 \\ Ours & REDAUNet & 0.9571 & 0.9029 & 0.9785 & 0.8971 & 0.8298 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with state-of-the-art models on the ISIC2017 dataset. layers of encoders and decoders, its prediction is relatively slow, especially in large-size image segmentation, which increases the computational complexity and time overhead. In addition, the training of the model requires a large number of labeled samples and complex preprocessing of the samples, which may be obstacles in terms of practical applications.