# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 4 Experiments and Results
### Comparative Experiments
#### 4.4.2 Comparison on the ISIC2017 Dataset  
We conducted experiments on the ISIC2017 dataset to test the performance of the proposed network in terms of generalization and robustness. A total of 300 randomly selected image samples were tested, and the model was compared with other advanced models. Table 6 shows the test results and Figure 11 shows the segmentation results of some lesions. Compared to others, our proposed model showed advantages in three out of five evaluation metrics, indicating higher performance in pixel-level segmentation. The REDAUNet model performed better than other comparative methods in _Dice_, _Spec_, and _mIoU_ metrics, surpassing the second-best model by 1.56%, 0.39%, and 2.31%, respectively. The _Acc_ and _Sens_ metrics are only slightly lower than the best result, with a difference of 0.15% and 2.04%, respectively. Figure 11 shows significant differences in the segmentation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Params** & **Train Time/Epoch** & **Test Time/Single Image** \\ \hline UNet & 36.13 & 41.31 & 0.07 \\ UNet3+ & 27.01 & 56.5 & 0.14 \\ Atten UNet & 42.25 & 47.42 & 0.10 \\ UNeXt & 4.3 & 10.68 & 0.02 \\ REDAUNet & 47.77 & 111 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different model parameters and operating times. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Model** & _Acc_ & _Dice_ & _Spec_ & _Sens_ & _mIoU_ \\ \hline
[39] & CPFNet & 0.9496 & 0.8769 & 0.9655 & 0.8953 & 0.7988 \\
[40] & DAGAN & 0.9324 & 0.8807 & 0.9588 & 0.9072 & 0.8113 \\
[41] & FAT-Net & 0.9578 & 0.8903 & 0.9699 & 0.9100 & 0.8202 \\
[42] & CKDNet & 0.9492 & 0.8779 & 0.9701 & 0.9055 & 0.8041 \\
[43] & TMUNet & 0.9603 & 0.9059 & 0.9746 & 0.9038 & 0.828 \\ Ours & REDAUNet & 0.9444 & 0.902 & 0.9774 & 0.9087 & 0.8323 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with state-of-the-art models on the ISIC2018 dataset. results of various models in lesion areas, especially in some results of UNet, UNet3+, and Atten UNet models, displaying issues of over-segmentation and insufficient generalizability. Although the UNeXt model has high similarity with the REDAUNet model in terms of segmentation effectiveness, their performance differs in detail. Overall, the proposed REDAUNet model shows good performance in segmenting lesions of different sizes and shapes, providing a reliable basis for its generalization performance in different datasets and demonstrating its superiority over some newer skin lesion segmentation algorithms. This section described the performance of our proposed model in detail. In order to evaluate the effectiveness of the proposed model for skin lesion segmentation, some experiments were performed. We further improved the segmentation performance of the model by setting up ablation experiments and comparing the evaluation results and segmentation effect map of each module, while also verifying the effectiveness of the proposed model in practice. Furthermore, we evaluated the segmentation performance of the proposed model with representative segmentation models in recent years from all aspects, and conducted a comprehensive analysis of five evaluation metrics (i.e., _Acc_, _Dice_, _Spec_, _Sens_, and _mloU_), while also comparing the predicted image effects of each model. The results show that the REDAUNet model has good performance on two datasets in terms of all the metrics. This means that the proposed model not only has good segmentation performance in skin lesion image segmentation tasks, but also has good generalization performance. In practical applications, the model can provide effective assistance in the segmentation process of computer-aided systems, achieving more satisfactory segmentation results and further improving the accuracy and efficiency of medical image processing. We compared the most recent methods of the past two years of research on skin lesion segmentation with our results, as shown in Table 7. Compared with other methods,