# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 4 Experiments
### Ablation Study  
To evaluate the effectiveness and importance of the proposed components, we conducted four ablation experiments on the Kvasir-SEG dataset. The results, summarized in Table 3, provide insights into the contribution of each module to the overall performance of MSEANet.  
The baseline achieved a mIoU of 82.92% and a mDice of 88.56%. When the CCF module was added, the performance improved significantly, with the mIoU increasing to 84.74%. This improvement highlights the CCF module's ability to integrate multi-scale contextual information effectively, enhancing the model's global understanding of polyp morphology.  
The inclusion of the EFE further boosted performance, with the mIoU reaching 85.34%. This result demonstrates the EFE module's role in capturing high-frequency edge details, enabling better delineation of polyp boundaries.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Kvasir-SEG**} & \multicolumn{6}{c}{**Kvasir-Sessile**} \\ \cline{2-10}  & _mIoU_ & _mDice_ & _Rec_ & _Prec_ & _F2_ & _mIoU_ & _mDice_ & _Rec_ & _Prec_ & _F2_ \\ \hline U-Net [5] & 76.82 & 84.01 & 86.38 & 88.14 & 84.69 & 24.72 & 36.88 & 72.37 & 32.64 & 46.35 \\ ColonSegNet [28] & 70.92 & 80.14 & 83.08 & 84.32 & 80.73 & 21.13 & 32.78 & 52.34 & 33.36 & 38.68 \\ DeepLabV3+ [17] & 80.44 & 87.48 & 88.74 & 90.18 & 87.68 & 59.27 & 70.78 & 70.85 & 82.25 & 70.09 \\ PraNet [9] & 83.02 & 89.8 & 90.60 & 91.64 & 90.09 & 66.71 & 77.36 & 80.69 & 82.44 & 78.71 \\ TGA-Net [11] & 83.30 & 89.82 & 91.32 & 91.23 & 90.29 & 69.23 & 79.78 & 79.35 & **84.88** & 79.89 \\
**MSEANet (Ours)** & **86.91** & **91.92** & **92.45** & **93.87** & **91.69** & **72.88** & **82.10** & **90.92** & 76.84 & **86.76** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with other advanced methods on the Kvasir-SEG and Kvasir-Sessile.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**BKAI**} \\ \cline{2-5}  & _mIoU_ & _mDice_ & _Rec_ & _Prec_ & _F2_ \\ \hline U-Net [5] & 75.99 & 82.86 & 82.95 & 89.89 & 82.64 \\ ColonSegNet [28] & 68.81 & 77.48 & 78.52 & 87.11 & 77.46 \\ DeepLabV3+ [17] & 83.14 & 89.37 & 88.70 & 92.30 & 88.82 \\ PraNet [9] & 82.64 & 89.04 & 89.01 & 92.47 & 88.85 \\ TGA-Net [11] & 84.09 & 90.23 & 90.26 & 92.08 & 90.02 \\
**MSEANet (Ours)** & **87.55** & **92.24** & **91.81** & **95.21** & **91.79** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons with other advanced methods on the BKAI.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & _mIoU_ & _mDice_ & _Rec_ & _Prec_ & _F2_ \\ \hline Baseline & 82.92 & 88.56 & 91.80 & 89.56 & 89.89 \\ +CCF & 84.74 & 90.23 & 92.02 & 91.94 & 91.60 \\ +EFE\&SEA & 85.34 & 90.49 & 91.33 & 93.0 & 90.45 \\ +ALL & **86.91** & **91.92** & **92.45** & **93.87** & **91.69** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of MSEANet on Kvasir-SEG.  
Similarly, the addition of the SEA improved performance, showcasing its capacity to fuse edge and global context information. The combined effect of the EFE and SEA modules reflects the model's increased sensitivity to edge features and its ability to enhance segmentation accuracy, particularly in challenging cases. Finally, when all modules--CCF, EFE, and SEA--were integrated, the model achieved its highest performance, with a mIoU of 86.91%, a mDice of 91.92%, and an F2-score of 91.69%. These results validate the complementary contributions of each module and demonstrate their synergistic effect in achieving superior segmentation outcomes.