## 4 Experiments
### Experimental Results
#### 4.3.5 Analysis  
Experimental evaluations on three publicly available datasets have demonstrated that MSLUnet outperforms other Unet models and their variants in terms of segmentation accuracy. This finding confirms the effectiveness and feasibility of our model's architecture. The superior segmentation performance of MSLUnet can be attributed to its utilization of a multiscale symmetric convolutional structure, which facilitates the extraction of diverse spatial features across various scales. The integration of a multi-scale feature extraction encoder enables the model to effectively capture both multi-scale features and global contextual information, thereby enhancing the accuracy of segmenting structures within medical images.  
Furthermore, the integration of a three-branch attention mechanism within the skip connections ensures that the model concentrates more effectively on the target area by suppressing irrelevant features. The qualitative results, illustrated in Figures 5-7, demonstrate MSLUnet's strong segmentation performance across targets of varying sizes. This proficiency is closely linked to the combination of a multi-scale convolutional block encoder and a large convolutional kernel block decoder. Together, they facilitate the acquisition of comprehensive representations that encompass essential low-level and high-level semantic features for medical image segmentation.  
Moreover, we plotted the mean Intersection over Union (mIoU) against the number of parameters and GFLOPs by averaging the segmentation performance across the three datasets. As shown in Figure 11, compared to lightweight segmentation networks such as UNeXt and LM-Net, MSLUnet has a higher number of parameters and consumes more computational resources. However, it significantly outperforms the existing state-of-the-art (SOTA) models in terms of image segmentation accuracy and processing complex medical images. This advantage is primarily attributed to its multi-scale feature extraction and three-branch attention mechanism, which are designed to significantly enhance the model's capacity to capture subtle features and adapt to structures of varying sizes. Consequently, our model effectively grasps global contextual information and multi-scale features, thereby improving its ability to accurately segment structures in medical images. However, it should be noted that the complex shapes and features of medical images may lead to the loss of deep edge refinement features. This loss can easily result in biased edge segmentation for images with intricate edges. As shown in Figure 7, our segmentation network performs better in accurately segmenting approximate regions compared to other networks. However, it struggles with segmenting complex edges.  
Figure 10: Box plot of Dice coefficient for multiple models on the ISIC_2018 dataset. This figure illustrates the distribution of the Dice coefficient obtained from four different trials. We conducted an analysis of variance (ANOVA) to evaluate the statistical significance of the variations among the different models. Boxes indicate the interquartile range (IQR), lines within the boxes indicate medians, and whiskers extend to 1.5 times the IQR.