## 4 Experiment and Results
### Model Generalization Ability Analysis  
**Setting:** In this section, we evaluate the model's generalization capabilities using three distinct datasets, namely, ETIS-LaribPolypDB, CVC-ColonDB, and CVC-300. As shown in Table 1, all three datasets, comprising a total of 318 images, were exclusively employed as the test set. Notably, unlike CVC-ClinicDB and Kvasir, the model underwent no prior training before being subjected to this test. **Results:** As illustrated in Tables 2, 4 and 5, the BMFA-Net model not only surpassed the existing methods but also demonstrated remarkable generalization capabilities. On CVC-ColonDB, it outperformed the RFPA model and the classical CaraNet by 0.4% and 7.6%, respectively. Similarly, on ETIS-LaribPolypDB, it surpassed RFPA and CaraNet by margins of 0.3% and 8.5%, respectively. Additionally, on CVC-300, it exhibited superior performance, where it was 2.0% and 1.7% better than CaraNet and RFPA, respectively. Furthermore, for a more intuitive visualization of the model comparison results, we provide line graphs depicting the mean Dice scores of the ten models across the CVC-300 dataset in Figure 5. Analyzing the reasons, the distinctive feature of CVC-300 is that it presents a smooth and single target. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**CVC-ColonDB**} & \multicolumn{8}{c}{**CVC-300**} \\  & **Mean Dice** & **Mean IoU** & \(F_{\beta}^{\omega}\) & \(S_{\mathbf{\mathrm{a}}}\) & \(E_{\mathbf{\varphi}}^{max}\) & **MAE** & **Mean Dice** & **Mean IoU** & \(F_{\beta}^{\omega}\) & \(S_{\mathbf{\mathrm{a}}}\) & \(E_{\mathbf{\varphi}}^{max}\) & **MAE** \\ \hline U-Net & 0.512 & 0.444 & 0.498 & 0.712 & 0.776 & 0.061 & 0.710 & 0.627 & 0.684 & 0.843 & 0.876 & 0.022 \\ U-Net++ & 0.483 & 0.410 & 0.467 & 0.691 & 0.760 & 0.064 & 0.707 & 0.624 & 0.687 & 0.839 & 0.898 & 0.018 \\ ACSNet & 0.716 & 0.649 & 0.697 & 0.829 & 0.851 & 0.039 & 0.863 & 0.787 & 0.825 & 0.923 & 0.968 & 0.013 \\ SAM & 0.468 & 0.422 & 0.463 & 0.690 & 0.608 & 0.054 & 0.726 & 0.676 & 0.729 & 0.849 & 0.826 & 0.020 \\ EU-Net & 0.756 & 0.681 & 0.730 & 0.831 & 0.872 & 0.045 & 0.837 & 0.765 & 0.805 & 0.904 & 0.933 & 0.015 \\ PraNet & 0.709 & 0.640 & 0.696 & 0.819 & 0.869 & 0.045 & 0.871 & 0.797 & 0.843 & 0.925 & 0.972 & 0.010 \\ SANet & 0.753 & 0.670 & 0.726 & 0.837 & 0.878 & 0.043 & 0.888 & 0.815 & 0.859 & 0.928 & 0.972 & 0.008 \\ Ours & **0.841** & **0.764** & **0.799** & **0.913** & **0.917** & **0.037** & **0.922** & **0.843** & **0.887** & **0.951** & **0.990** & **0.004** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative results of test datasets from CVC-ColonDB and CVC-300. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Kvasir**} & \multicolumn{8}{c}{**CVC-ClinicDB**} \\  & **Mean Dice** & **Mean IoU** & \(F_{\beta}^{\omega}\) & \(S_{\mathbf{\mathrm{a}}}\) & \(E_{\mathbf{\varphi}}^{max}\) & **MAE** & **Mean Dice** & **Mean IoU** & \(F_{\beta}^{\omega}\) & \(S_{\mathbf{\mathrm{a}}}\) & \(E_{\mathbf{\varphi}}^{max}\) & **MAE** \\ \hline U-Net & 0.818 & 0.746 & 0.794 & 0.858 & 0.893 & 0.055 & 0.823 & 0.755 & 0.811 & 0.889 & 0.954 & 0.019 \\ U-Net++ & 0.821 & 0.743 & 0.808 & 0.862 & 0.910 & 0.048 & 0.794 & 0.729 & 0.785 & 0.873 & 0.931 & 0.022 \\ ACSNet & 0.898 & 0.838 & 0.882 & 0.920 & 0.952 & 0.032 & 0.882 & 0.826 & 0.873 & 0.927 & 0.959 & 0.011 \\ SAM & 0.782 & 0.710 & 0.773 & 0.832 & 0.836 & 0.061 & 0.579 & 0.526 & 0.563 & 0.744 & 0.685 & 0.057 \\ EU-Net & 0.908 & 0.854 & 0.893 & 0.917 & 0.954 & 0.028 & 0.902 & 0.846 & 0.891 & 0.936 & 0.965 & 0.011 \\ PraNet & 0.898 & 0.840 & 0.885 & 0.915 & 0.948 & 0.030 & 0.899 & 0.849 & 0.896 & 0.936 & 0.979 & 0.009 \\ SANet & 0.904 & 0.847 & 0.892 & 0.915 & 0.953 & 0.028 & 0.916 & 0.859 & 0.909 & 0.939 & 0.976 & 0.012 \\ Ours & **0.923** & **0.875** & **0.915** & **0.933** & **0.972** & **0.021** & **0.934** & **0.887** & **0.942** & **0.959** & **0.994** & **0.005** \\ \hline \hline \end{tabular}
\end{table}