## 4 Experiments
### Experimental Results
#### 4.3.4 Statistical Test Analysis
* ISIC_2018 dataset: The F-statistic is 62.44, and the \(p\)-value is 1.62 \(\times\) 10\({}^{-18}\), indicating the smallest difference in performance between the models compared to the previous two datasets, albeit still significant. In order to make the comparison of results more intuitive, we visualize the results using box plots. From the experimental results, we can see that our proposed MSLUnet demonstrates optimal performance compared to all other models, showing high stability and accuracy. From Figure 8, we can observe significant differences in the performance of various medical image segmentation models based on the Dice coefficient. Among these, MSLUnet and LM-Net perform optimally, exhibiting the highest median values and a more concentrated distribution. This indicates that these two models have higher consistency and stability in medical image segmentation tasks. MSLUnet slightly outperforms LM-Net, evidenced by a higher upper limit of Dice scores, suggesting that MSLUnet can achieve superior segmentation accuracy in some instances. AAU-Net and SwinUnet also demonstrate commonable performance, with their median and distribution of Dice scores indicating efficient image segmentation capabilities, albeit slightly less so than MSLUnet and LM-Net. ResUnet++ and UNet++ display moderate performance, characterized by median values in the middle range and a relatively wide distribution, which suggests that their performance may fluctuate widely across test samples. UNet and CMUNeXt perform slightly below the middle of the range but remain within acceptable limits. Conversely, Attention_Unet, ULite, and ConvUNeXt exhibit poorer performance. From the box plots depicted in Figure 9, it is evident that our model, MSLUnet, achieves the highest Dice scores, exhibiting the highest median and a compact distribution across the four trials, which indicates stable and excellent performance. GCASCADE and CMUNeXt also exhibit high Dice scores, especially GCASCADE, whose upper quartile nearly reaches 92%, showing very high segmentation accuracy. LM-Net and AAU-Net also demonstrate outstanding performance, with Dice scores over 90%, albeit slightly lower than those of GCASCADE and CMUNeXt. In contrast, SwinUnet shows relatively poorer performance, with a wide distribution of scores that reach a low of approximately 83%, potentially leading to unstable performance in certain cases. ULite and Attention_Unet perform in the low to mid-range, suggesting potential limitations in handling complex medical image segmentation tasks. \begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **F-Statistic** & **\(p\)-Value** & **Conclusion** \\ \hline BUSI [39] & 302.87 & 1.78 \(\times\) 10\({}^{-29}\) & Significant difference \\ Kvasir-SEG [40] & 126.46 & 2.39 \(\times\) 10\({}^{-23}\) & Significant difference \\ ISIC 2018 [41] & 62.44 & 1.62 \(\times\) 10\({}^{-18}\) & Significant difference \\ \hline \end{tabular}
\end{table} 
Table 5: Results of ANOVA analysis of multiple models on different datasets. From Figure 10, we can observe the Dice coefficient performance of various models in the medical image segmentation task. The MSLUnet model in this paper demonstrates the highest scores on all Dice coefficients with less volatility. This indicates that MSLUnet demonstrates high consistency and accuracy in medical image segmentation and is the top performer among all the models. The two models, UNeXt and GCASCADE, also achieve high scores close to that of MSLUnet, but the volatility of GCASCADE is slightly higher. The scores of UNeXt range from 89.2 to 90.3, while GCASCADE's scores range from 89.4 to 90.9. These two models also demonstrate good segmentation results and are suitable for medical image processing that demands higher accuracy. Both the TransUnet and CMUNeXt models consistently deliver high performance. The TransUnet scores range from 87.8 to 88.3, while the CMUNeXt scores range from 88.1 to 88.5. These models may be slightly inferior when dealing with complex medical images, but they are still reliable choices.