## 4 Experiments
### Experimental Results
#### 4.3.4 Statistical Test Analysis  
In an analysis of variance (ANOVA), the F-statistic and the \(p\)-value are two key indicators that can be used to assess whether differences between clusters are statistically significant. The F-statistic is used to compare the ratio of the difference between the means of multiple clusters to the within-group difference. In the ANOVA test, the F-statistic is calculated as follows:  
\[\text{F}=\frac{\text{MSB}}{\text{MSW}} \tag{13}\]  
where MSB (Mean Square Between groups) represents the variance between groups and MSW (Mean Square Within groups) represents the variance within groups. The larger the F-value, the greater the variance between groups, indicating a more significant difference in the means of different clusters. The \(p\)-value represents the probability of observing the F-statistic or a more extreme value if the original hypothesis (i.e., assuming that the means of all clusters are equal) is true; \(p\)-values are used as a measure of the consistency of the data with the original hypothesis. Smaller \(p\)-values imply that the probability of observing the experimental data, if the original hypothesis is true, is lower. This justifies the rejection of the original hypothesis to a greater extent; \(p\)-values can range from 0 to 1. Typically, the \(p\)-value will be lower than the significance level \(\alpha\) (we chose \(\alpha\) to be 0.01). If the \(p\)-value is less than \(\alpha\), the statistical results are considered statistically significant, and the original hypothesis is rejected, indicating significant differences between clusters. In order to more accurately compare the performance of each model, we tested the differences in the model Dice coefficient using analysis of variance (ANOVA) to determine if there were statistically significant differences in model performance. The purpose of this experiment was to evaluate the performance of various medical image segmentation models in processing the three medical image datasets we utilized and to compare their accuracy and stability. We compared several popular deep-learning segmentation models in Tables 2-4. Each model was trained on the same hardware and software configurations, using the same number of training rounds and learning rates. To evaluate the stability of each model, four independent runs were conducted under the same initialization conditions, with each run employing a distinct partition of images for assessment. This methodology is designed to ensure the reliability of the results and to mitigate errors potentially caused by biases in data selection. The final ANOVA analysis results are shown in Table 5.  
Figure 7: Qualitative comparison results of U-Net, U-Net++, Attention U-Net, Res-U-Net++, TransSUNet, ConvUNeXt, CMUNeXt, UNeXt, GCASCADE, and MSLUnet on the ISIC 2018 dataset. The blue boxes display the results of our designed MSLUnet network segmentation, while the red boxes indicate the segmentation regions where our model outperforms the other models. From the analysis results in Table 5, it can be seen that the F-statistic values of each dataset are very high, and the corresponding \(p\)-values are also very small (much smaller than the commonly used significance level of 0.01). This indicates that there are obvious differences in the performance of various models on different datasets. In other words, different models exhibit varying adaptability and effectiveness on different datasets. * BUSI dataset: The F-statistic is 302.87 with a \(p\)-value of 1.78 \(\times\) 10\({}^{-29}\), indicating an extremely significant difference in performance between models on this dataset. This could mean that some models perform very well on this type of data while others perform very poorly with high variability. * Kvasir-SEG dataset: The F-statistic is 126.46 with a \(p\)-value of 2.39 \(\times\) 10\({}^{-23}\), indicating lower variability in performance among models compared to the BUSI dataset. However, the significance level remains very high.