## 3 Experiments and Analysis
### Experimental Results and Analysis
#### 3.4.6 Effectiveness of Multi-Scale Multi-Network Integration Based on Transfer Learning  
In order to prove the effectiveness of the proposed method in retinal blood vessel segmentation, this paper carried out experiments on two classical retinal image datasets and compared with the current seven most advanced methods, which are shown in Tables 16 and 17.  
It can be seen from Table 16, in the DRIVE dataset, the proposed method has the second-best results, whose Acc achieves 96.74% and SE achieves 81.48%. Compared with the classic U-Net, Acc and SE are improved by 0.32% and 2.33%, respectively. The experiment results prove the effectiveness of the proposed method.  
It can be seen from Table 17, in the CHASE_DB1 dataset, the proposed method achieves acceptable results. The ACC achieves 97.38% and the SE achieves 78.22%. Compared with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Methods** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline \multirow{6}{*}{DRIVE} & U-Net [18] & 96.40 & 79.15 & 98.08 \\  & R2U-Net [25] & 96.54 & 79.23 & 98.03 \\  & Attu-Net [30] & 96.49 & 78.82 & 98.48 \\ \cline{1-1}  & CE-Net [31] & 96.99 & 80.15 & 98.16 \\ \cline{1-1}  & IterNet [32] & 96.57 & 79.95 & 98.26 \\ \cline{1-1}  & NFN+ [33] & 96.68 & 80.02 & 97.90 \\ \cline{1-1}  & SCS-Net [17] & **96.97** & **82.89** & 98.38 \\ \cline{1-1}  & Ours & **96.74** & **81.48** & **98.27** \\ \hline \hline \end{tabular}
\end{table}
Table 16: The result of proposed method compared with the other methods in the DRIVE dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Methods** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline \multirow{6}{*}{CHASE\_DB1} & U-Net [18] & 97.16 & 76.17 & 98.61 \\  & R2U-Net [25] & 97.21 & 81.45 & 98.40 \\  & Attu-Net [30] & 97.26 & 77.21 & 98.50 \\ \cline{1-1}  & CE-Net [31] & 97.23 & 80.42 & 98.39 \\ \cline{1-1}  & IterNet [32] & 97.31 & 79.97 & 98.47 \\ \cline{1-1}  & NFN+ [33] & 97.35 & 79.33 & 98.55 \\ \cline{1-1}  & SCS-Net [17] & **97.44** & **83.65** & 98.39 \\ \cline{1-1}  & Ours & **97.38** & **78.22** & **98.80** \\ \hline \hline \end{tabular}
\end{table}
Table 17: The result of proposed method compared with the other methods in the CHASE_DB1 dataset.  
Figure 6: Experimental results of different methods in the DRIVE dataset: (**a**) random initialization; (**b**) finetuning classifier; (**c**) finetuning whole network; (**d**) three identical networks integration; (**d**) three different networks integration; (**e**) integration of multiple networks with three scales; (**f**) integration of multiple networks with five scales; (**g**) integration of multiple networks with seven scales; (**h**) ground truth.  
the classical U-Net, the ACC increases by 0.22% and SE increases by 2.05%, which proves the effectiveness of the proposed method. SCS net has achieved good results in retinal blood vessel segmentation, but the model proposed in this paper provides a new idea for retinal image segmentation. It can segment blood vessels more accurately by obtaining multi-scale information of different regions, and experiments show that this idea is effective.