# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 4 Experiments
### Results  
As shown in Figure 5, in situations where lighting is uneven and polyps are small and difficult to discern, the model performs exceptionally well. This is primarily due to its dedicated EFE module, which ensures the enhancement of edge information capture even under challenging lighting conditions. In contrast, other methods, such as DeepLabv3+, may be adversely affected by such conditions, failing to accurately capture the boundary information. Notably, in images containing multiple polyps, MSEANet accurately segments each polyp region. This success stems from the model's robust understanding of global contextual information and its effective integration of edge and global context features during the decoding stage. The use of multi-scale features effectively compensates for limitations in the receptive field, ensuring that even small polyps are accurately segmented.  
Furthermore, during the decoding phase, the model effectively fuses multi-scale information, allowing it to accurately segment multiple polyps. This capability ensures that the model does not miss any polyps. This design of MSEANet not only significantly expands the model's receptive field, but also enhances its capability to accurately segment small polyps and identify multi-polyp regions.  
The quantitative results are summarized in Tables 1 and 2. In this study, MSEANet was compared with several state-of-the-art methods commonly used in polyp segmentation, including U-Net [5], ColonSegNet [28], Deeplabv3+ [17], PraNet [9], and TGA-Net [11]. These algorithms hold substantial importance in the field.  
**Comparison on Kvasir-SEG:** As shown in Table 1, MSEANet outperforms other state-of-the-art methods on the Kvasir-SEG dataset across all metrics. Notably, it achieves a mIoU of 86.91% and a mDice of 91.92%, critical performance indicators in the medical domain. Additionally, other metrics such as Recall, Precision, and F2-score also reflect the model's advanced capabilities.  
**Comparison on Kvasir-Sessile:** The Kvasir-Sessile dataset holds substantial clinical relevance as it includes challenging flat and sessile polyps [11]. As presented in Table 1, MSEANet surpasses all other methods, achieving a mIoU of 72.88%. Compared to PraNet [9], the mIoU increased by nearly 6%. These results underscore MSEANet's robustness and effectiveness in handling clinically significant yet difficult polyp types.  
**Comparison on BKAI:** Table 2 showcases the comparative results on the BKAI dataset, where MSEANet achieves a mIoU of 87.55% and a mDice of 92.24%, along with an F2-score of 91.79%. These metrics highlight the model's ability to excel even in diverse and complex clinical scenarios. The high performance validates the model's ability to segment polyp regions accurately while significantly reducing the false negative rate. By extracting and effectively fusing features across multiple scales and receptive fields, MSEANet demonstrates a clear advantage over other advanced methods.  
**Model Result Analysis:** The results demonstrate that MSEANet achieves excellent performance across multiple datasets, which can be attributed to its unique architecture.  
Figure 5: Comparison results of MSEANet with other advanced models on the Kvasir-SEG.  
Specifically, MSEANet incorporates dedicated modules for processing edge information and multi-scale contextual features. During the decoder phase, these features are efficiently fused through specialized mechanisms, allowing the model to capture both fine-grained details and global context. As shown in Figure 5, MSEANet excels in handling challenging scenarios, including blurry edges, multiple polyps, and complex backgrounds, thanks to its edge-aware and multi-scale information fusion capabilities. In contrast, other methods may struggle in these specific situations due to the absence of specialized edge-awareness modules or inadequate integration of global context. These architectural differences highlight why MSEANet outperforms other advanced models.