# A Multi-Scale Attention Fusion Network for Retinal Vessel Segmentation
## 4 Experiments and Results
### Results
#### 4.3.2 Comparison with State-of-the-Art Segmentation Methods  
The comparison of the performance between the proposed model and state-of-the-art methods on the DRIVE dataset is shown in Table 5. Compared with the other advanced models, MsAF achieved the highest sensitivity value of 0.8611 and the highest F1-score value of 0.8383 on the DRIVE dataset, surpassing the highest values obtained by the other advanced models by 0.14% and 0.26%, respectively. The differences in specificity, accuracy, and AUC compared with the highest values obtained by the other advanced models were 0.19%, 0.72%, and 1.15%, respectively. The comparison of the performance between the proposed model and state-of-the-art methods on the CHASE_DB1 dataset is shown in Table 6. Compared with the other advanced models, MsAF achieved the highest sensitivity value of 0.8630 and the highest F1-score value of 0.8435 on the DRIVE dataset, surpassing the highest values obtained by the other advanced models by 0.57% and 0.85%, respectively. The differences in specificity, accuracy, and AUC compared with the highest values obtained by the other advanced models were 0.46%, 0.34%, and 0.94%, respectively. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **SE** & **F1** & **SP** & **ACC** & **AUC** \\ \hline U-Net [10] & 0.8320 & 0.8217 & 0.9814 & 0.9644 & 0.9758 \\ Unet++ [33] & 0.8425 & 0.8253 & 0.9829 & 0.9650 & 0.9764 \\ Attention U-Net [34] & 0.8349 & 0.8236 & 0.9817 & 0.9637 & 0.9779 \\ The proposed model & 0.8614 & 0.8511 & 0.9866 & 0.9754 & 0.9845 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The segmentation results on the STARE dataset. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **SE** & **F1** & **SP** & **ACC** & **AUC** \\ \hline [PERSON] et al. [35] & 0.7894 & 0.8203 & 0.9870 & 0.9697 & 0.9856 \\ [PERSON] et al. [36] & 0.7891 & 0.8249 & 0.9804 & 0.9561 & 0.9806 \\ [PERSON] et al. [37] & 0.7940 & 0.8270 & 0.9816 & 0.9567 & 0.9772 \\ [PERSON] et al. [38] & 0.7791 & 0.8218 & 0.9831 & 0.9574 & 0.9813 \\ [PERSON] et al. [39] & 0.8215 & 0.8267 & 0.9845 & 0.9701 & 0.9867 \\ [PERSON] et al. [40] & 0.8520 & 0.8297 & - & 0.9555 & 0.9814 \\ [PERSON] et al. [41] & 0.8324 & - & 0.9757 & 0.9574 & 0.9820 \\ [PERSON] et al. [42] & 0.8071 & 0.8251 & 0.9782 & 0.9565 & 0.9801 \\ [PERSON] et al. [43] & 0.8597 & - & 0.9690 & 0.9547 & 0.9837 \\ [PERSON] et al. [44] & 0.8361 & 0.8287 & 0.9740 & 0.9563 & 0.9799 \\ [PERSON] et al. [26] & 0.8212 & 0.8263 & 0.9840 & 0.9698 & 0.9864 \\ [PERSON] et al. [45] & 0.8291 & 0.8302 & 0.9852 & 0.9622 & 0.9859 \\ [PERSON] et al. [46] & 0.8302 & 0.8018 & 0.9826 & 0.9581 & 0.9821 \\ [PERSON] et al. [47] & 0.8056 & 0.8357 & 0.9854 & 0.9680 & 0.9875 \\ The proposed model & 0.8611 & 0.8383 & 0.9851 & 0.9629 & 0.9760 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results on the DRIVE dataset. Examining the green box in Figure 7, it is observed that the proposed model accurately identified this vessel more completely. Unet++ recognized only a small portion, while U-Net and Attention U-Net almost failed to identify this vessel, resulting in false negatives. In the red box of the ground truth in Figure 7, representing a completely background area, all models recognized an incomplete vessel, leading to false positives. Figures 8-10 display the differential images of segmentation results on a different dataset each. Through the analysis of the differential images, we can distinctly observe the distribution of false positives and false negatives across different models and datasets. Upon comparing the differential images of different models, as depicted in Figures 8-10, it is evident that the proposed model exhibited lower false positives and false negatives compared with the baseline model across all three datasets. Furthermore, contrasting the differential images of different datasets revealed significant variations in the quantities of false positives and false negatives.