## 3 Skin Lesion Experiments and Analysis
### Ablation Study  
To assess the effectiveness of FuzzyTransNet in the context of melanoma skin lesion segmentation, we undertook an evaluation that encompassed various Transformer branches and CNN branches. This evaluation was conducted through the utilization of both sequential and parallel connections, employing the Dice coefficient as our benchmark metric. As demonstrated in Table 4, when comparing the results of E.1, E.2, and E.3, we can discern a marginal performance gain with ResNet50 over ResNet34. Furthermore, it is noteworthy that the Swin Transformer V2 outperformed DeiT-Small [36] in terms of performance. When juxtaposing E.3 with E.6, it becomes evident that the parallel models exhibited superior performance over the sequential models. Moreover, when contrasting E.6 with E.4 and E.5, we can see that the Swin Transformer V2, in contrast to the other Transformers, effectively translated low-resolution images and pre-trained models from a windowed perspective to higher resolutions, thereby further amplifying the overall performance. Additionally, when comparing the results of E.4, E.6, and E.7, we can glean  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **JI** & **Dice** & **ACC** \\ \hline U-Net [6] & 2015 & 0.763 & 0.847 & 0.928 \\ CDNN [27] & 2017 & 0.765 & 0.849 & 0.934 \\ PDN [28] & 2018 & 0.765 & 0.866 & 0.940 \\ FFNN [29] & 2018 & 0.771 & 0.871 & 0.940 \\ SLSDeep [30] & 2018 & 0.782 & 0.878 & 0.936 \\ DCL-PSI [31] & 2019 & 0.777 & 0.857 & 0.941 \\ U-Net++ [7] & 2019 & 0.773 & 0.855 & 0.938 \\ TransFuse [32] & 2021 & 0.795 & 0.872 & 0.944 \\ Ms RED [33] & 2022 & 0.786 & 0.865 & 0.941 \\
**Our method** & - & **0.799** & **0.886** & **0.955** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results on the ISIC2017 test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Year** & **JI** & **Dice** & **ACC** \\ \hline U-Net [6] & 2015 & 0.755 & 0.824 & 0.895 \\ U-net++ [7] & 2019 & 0.761 & 0.851 & 0.925 \\ DCL-PSI [31] & 2019 & 0.763 & 0.847 & 0.929 \\ DO-Net [34] & 2020 & 0.794 & 0.854 & 0.941 \\ CA-Net [35] & 2020 & 0.794 & 0.874 & 0.948 \\ TransFuse [32] & 2021 & 0.791 & 0.869 & 0.942 \\ Ms RED [33] & 2022 & 0.793 & 0.876 & 0.946 \\
**Our method** & - & **0.801** & **0.889** & **0.961** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results on the ISIC2018 test set.  
that with a mere replacement of dual branches, FuzzyTransNet's performance surpassed that of TransFuse. Notably, with the integration of the fuzzy block, the Dice coefficient was significantly enhanced. Within the context of the ISIC2017 and ISIC2018 datasets, E.7 exhibited increments of 0.7% and 0.8%, respectively, compared to E.6.  
Additional experiments conducted on the ISIC2018 dataset are presented in Table 5. After we used ResNet50 and the Swin Transformer V2 as the two branches, the three evaluation indicators increased by 0.6%, 1.1%, and 0.9%, respectively, compared to ResNet34 and DeiT-S, which were used by TransFuse. After introducing the fuzzy block constructed in this paper, the result after changing the branch was further improved by 0.4%, 0.8%, and 0.7%. Finally, the data augmentation model was added to achieve the optimal result.