## 6 Result and Discussion
### Comparison with Existing Retina Segmentation Models  
In this section, we compare the performance of our proposed model with several state-of-the-art retina segmentation models, specifically focusing on their specificity across three widely used datasets: DRIVE, CHASE_DB1, and STARE. The models included in the comparison are MCDAU-Net (2023) [40], ResDO-UNet (2022) [41], ARSA-UNet (2024) [43], SMP-Net (2024) [44], and Gabor-Net (2024) [45]. Specificity is a crucial metric in segmentation tasks, as it measures the model's ability to correctly identify the negative class, thus reducing the number of false positives.  
The bar graph (Figure 5) presents a comparative analysis of the specificity values for each model across the three datasets. Our proposed model achieves the highest specificity across all datasets. On the DRIVE dataset, the proposed model achieves a specificity of 0.9826, outperforming SMP-Net (2024), which records a specificity of 0.9813. Similarly, on the CHASE_DB1 dataset, our model records a specificity of 0.9892, significantly higher  
Figure 3: Qualitative analysis of the proposed network and its comparison with existing segmentation baseline models.  
than the next best model, SMP-Net (2024), which has a specificity of 0.9876. On the STARE dataset, our proposed model again leads with a specificity of 0.981, surpassing ResDO-UNet (2022), which has a specificity of 0.9792.  
Figure 4: Qualitative analysis of the proposed network and its comparison with existing segmentation baseline models on ISO-STAR, HRF, and LES-AV datasets.  
These results clearly demonstrate the superiority of our proposed model over the existing models, highlighting its robustness and reliability in retinal vessel segmentation tasks. These results are illustrated in Table 3 for STARE, DRIVE, and CHASE datasets. The high specificity values indicate that our model is highly effective in correctly identifying non-vessel regions, therefore reducing the incidence of false positives. This capability is crucial for enhancing the accuracy and reliability of diagnostic tools in ophthalmology.  
The accompanying bar graph visually reinforces these comparisons, providing a clear and concise representation of the superior performance of our proposed model across the three datasets. The consistent outperformance of our model underscores its potential to set a new standard in retina segmentation, improving diagnostic outcomes in clinical settings.  
Figure 5: Comparative Analysis of Proposed Model with Recently Published Retina Segmentation Models. The bar graph illustrates the specificity of various state-of-the-art models MCDAU-Net (2023) [40], ResDO-UNet (2022) [41], ARSA-UNet (2024) [43], SMP-Net (2024) [44], Gabor-Net (2024) [45], SGAT-Net(2023) [51], and TUnet-LBF(2023) [52] across the DRIVE, CHASE_DB1, and STARE datasets. Our proposed model consistently outperforms the other models in specificity, achieving the highest values across all datasets. The graph highlights the superior ability of our model to correctly identify non-vessel regions, therefore minimizing false positives and enhancing diagnostic accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Year**} & **DRIVE** & **CHASE\_DB1** & **STARE** \\ \cline{3-5}  & & **Specificity** & **Specificity** & **Specificity** \\ \hline ResDO-Unet & 2022 & 0.9791 & 0.9794 & 0.9792 \\ \hline MCDAU-Net & 2023 & 0.9739 & 0.9694 & 0.9686 \\ \hline SGAT-NET & 2023 & 0.9762 & 0.9863 & 0.9832 \\ \hline TUnet-LBF & 2023 & 0.9809 & 0.9806 & **0.9852** \\ \hline ARSA-Unet & 2024 & 0.9729 & 0.9753 & 0.9772 \\ \hline SMP-Net & 2024 & 0.9813 & 0.9876 & 0.9753 \\ \hline Gabor-Net & 2024 & 0.9721 & 0.9824 & 0.9714 \\ \hline Proposed Model & 2024 & **0.9826** & **0.9892** & 0.9810 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Comparison of Proposed Network with Comparative Models.