## 4 Experiments
### Experimental Results
#### 4.3.6 Ablation Experiments  
To fully evaluate the effectiveness of our proposed MSLUnet, we conducted comprehensive ablation experiments on three datasets to analyze the contribution of each module. In the ablation experiments, we selected the classical UNet network as the baseline network, and Table 6 presents the experimental results of different components on each dataset.  
In our ablation studies, we initially incorporated the Multiscale Feature Extraction Encoder (MSE) module into the traditional UNet architecture. Experimental results clearly demonstrate that this integration not only improves segmentation performance but also decreases the number of parameters and computational load. This improvement is primarily due to the MSE block's use of a continuous 3 \(\times\) 3 small convolutional kernel, which efficiently captures diverse multi-scale feature information and addresses potential seman  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Parameter (M)**} & \multirow{2}{*}{
\begin{tabular}{c} **FLOPs** \\ **(G)** \\ \end{tabular} } & \multicolumn{2}{c}{**BUSI**} & \multicolumn{2}{c}{**Kvasir-SEG**} & \multicolumn{2}{c}{**ISIC 2018**} \\ \cline{3-10}  & & & **mIoU** & \multirow{2}{*}{**Dice**} & **mIoU** & \multirow{2}{*}{**Dice**} & **mIoU** & \multirow{2}{*}{**Dice**} \\ \cline{3-10}  & & & **(\%)** & & & **(\%)** & & **(\%)** & \\ \hline UNet & 4.32 & 35.61 & 77.1 & 0.737 & 87.3 & 0.866 & 85.1 & 0.848 \\ UNet + MSE & 3.86 & 10.55 & 78.2 & 0.779 & 89.9 & 0.897 & 86.9 & 0.896 \\ UNet + LKE & 2.64 & 5.21 & 77.9 & 0.764 & 91.8 & 0.904 & 86.5 & 0.893 \\ UNet + AT & 4.31 & 10.15 & 78.0 & 0.767 & 90.6 & 0.901 & 87.2 & 0.884 \\ MSLUnet (our) & **2.18** & **5.69** & **79.3** & **0.798** & **91.1** & **0.932** & **87.3** & **0.914** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ablation experiments for each module on three datasets.  
Figure 11: (**a**) shows the comparison of the parameters of each segmentation model, and (**b**) shows the comparison of FLOPs of each segmentation model. The blue box indicates the MSLUnet network of this paper.  
tic gaps in the model. As a result, it leads to a more effective feature representation and a decrease in redundant parameters.  
Subsequently, we introduced the Large-Kernel Convolution Feature Extraction (LKE) module into the baseline network, which significantly improved the segmentation performance. The LKE module efficiently extracts global contextual information by utilizing deeply separable large convolutional kernels, thereby enhancing the network's feature fusion capabilities.  
Moreover, there is a significant semantic gap between shallow and deep features; simple concatenation can result in the loss of some important features. The integration of a lightweight three-branch attention mechanism into the skip connections significantly enhances the capture of medical information by effectively leveraging global contextual information from both shallow feature spaces and channels. This allows the model to more effectively focus on features relevant to the current task, thus enhancing information transfer and accurately capturing medical semantic and structural details.  
Overall, our ablation experiments confirm that each module in the proposed MSLUnet architecture significantly contributes to enhanced segmentation performance while maintaining a relatively low number of parameters and reducing computational demand.