## 4 Experiments and Results
### Comparative Analysis of Polyp Segmentation Performance Across Diverse Imaging Scenarios  
To verify the segmentation performance of the HPANet for polyps in different scenarios, based on the experimental results in Sections 4.2 and 4.3, five representative imaging scenes were selected from five datasets to observe the segmentation results, with the segmentation outcomes visualized in Figure 5. The image (Row 1, Column 1) demonstrates large polyps with partial blurred margins. Conventional segmentation models (U-Net, ResUnet, PraNet) exhibit incomplete localization of polyp margins, while CaraNet and Polypp-PVT generate boundaries with reduced sharpness. In contrast, the proposed HPANet can accurately locate the polyp and produce clear and complete segmentation boundaries. The image (Row 2, Column 1) was selected from the CVC-ClinicDB dataset, where the polyps exhibit adhesions but have relatively clear boundaries. Compared to the other five models, the HPANet matches the manually annotated results very well in terms of overall shape and position, and the segmentation boundaries are also smoother. The image (Row 3, Column 1) selected from the CVC-300 dataset is characterized by the presence of artifacts and glare caused by the camera's movement in polyps. The U-Net and Polypp-PVT segmentation models may produce incorrect segmentation, while ResUnet, PraNet, and CaraNet can roughly locate the position of polyps, but artifacts may appear near the segmentation boundary. The image (Row 4, Column 1) is from the CVC-ColonDB dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Model** & **mIoU** & **mDice** & **Recall** & **Precision** & **F2** \\ \hline \multirow{8}{*}{CVC-ClinicDB} & U-Net [3] & 0.4051 & 0.4902 & 0.4534 & 0.7521 & 0.4619 \\  & ResUnet [34] & 0.5558 & 0.6314 & 0.6059 & 0.7899 & 0.6128 \\  & PraNet [7] & 0.7382 & 0.8029 & 0.8218 & 0.8228 & 0.8082 \\  & CaraNet [35] & 0.5578 & 0.6213 & 0.7612 & 0.6262 & 0.6658 \\  & Polypp-PVT [19] & 0.7273 & 0.8045 & 0.8301 & 0.836 & 0.8154 \\  & HPANet & **0.7539** & **0.8264** & **0.8632** & **0.8586** & **0.8397** \\ \hline \multirow{8}{*}{CVC-300} & U-Net [3] & 0.2224 & 0.3025 & 0.2389 & 0.7127 & 0.2584 \\  & ResUnet [34] & 0.7278 & 0.8041 & 0.8207 & 0.822 & 0.8122 \\ \cline{1-1}  & PraNet [7] & 0.7879 & 0.8621 & 0.9423 & 0.8182 & 0.9007 \\ \cline{1-1}  & CaraNet [35] & 0.7269 & 0.7962 & 0.8622 & 0.8178 & 0.8217 \\ \cline{1-1}  & Polypp-PVT [19] & 0.7799 & 0.8587 & 0.9404 & 0.8232 & 0.8978 \\ \cline{1-1}  & HPANet & **0.827** & **0.8965** & **0.9613** & **0.8563** & **0.9302** \\ \hline \multirow{8}{*}{CVC-ColonDB} & U-Net [3] & 0.1855 & 0.252 & 0.2301 & 0.5649 & 0.2273 \\ \cline{1-1}  & ResUnet [34] & 0.4806 & 0.5507 & 0.5226 & 0.7056 & 0.5307 \\ \cline{1-1}  & PraNet [7] & 0.556 & 0.6327 & 0.7078 & 0.6435 & 0.6486 \\ \cline{1-1}  & CaraNet [35] & 0.4987 & 0.5635 & 0.6045 & 0.6488 & 0.5714 \\ \cline{1-1}  & Polypp-PVT [19] & 0.6913 & 0.7773 & 0.7995 & **0.817** & 0.7842 \\ \cline{1-1}  & HPANet & **0.7044** & **0.7867** & **0.815** & 0.8099 & **0.7953** \\ \hline \multirow{8}{*}{ETIS-LaribPolypDB} & U-Net [3] & 0.257 & 0.3053 & 0.312 & 0.4734 & 0.2977 \\ \cline{1-1}  & ResUnet [34] & 0.4656 & 0.5327 & 0.5519 & 0.6082 & 0.5386 \\ \cline{1-1}  & PraNet [7] & 0.4626 & 0.5239 & 0.5881 & 0.5204 & 0.5461 \\ \cline{1-1}  & CaraNet [35] & 0.4026 & 0.4782 & 0.4778 & 0.6013 & 0.4676 \\ \cline{1-1}  & Polypp-PVT [19] & 0.606 & 0.6862 & 0.7436 & **0.7361** & 0.7131 \\ \cline{1-1}  & HPANet & **0.6268** & **0.6965** & **0.7458** & 0.7061 & **0.7198** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results on CVC-ClinicDB, CVC-300, CVC-ColonDB and ETIS-LaribPolypDB. The best results have highlighted in bold. The contrast between the polyps in this image and the surrounding mucosa is low, leading to segmentation errors in U-Net, ResUnet, PraNet, and CaraNet, while Polyp-PVT and HPANet only show a certain degree of under segmentation.