## 5 Experiments
### Comparative Experiments  
We compare our network with the state-of-the-art models, such as U-Net, LadderNet, Dynamic Deep Networks, and IterNet, respectively, on three data sets, and use the standard evaluation method to evaluate. Figure 6 shows the segmentation details of U-Net [3], LadderNet [16], Dynamic Deep Network [17], and our network on the three data sets of DRIVE, ELE, and CORO. See from a partially enlarged view of the image, our network can distinguish the retinal blood vessels from the background noise, and there is less noise in the boundary part of the eyeball. From the results of the segmentation of the ELE data set, our network can distinguish between thinner and darker one's vascular structure. For the CORO data set, our segmentation made the vascular structure clearer and has fewer artifacts. Compared with other  
Figure 5: Complex vessel segmentation results. From left to right are the DRIVE, CORO, and ELE datasets. We compared the three data sets to verify the effect of our improved de-normalization structure on vascular segmentation. The approach with fixup means that we replace the original Basicblock with Fixupblock and do not use the channel attention mechanism. The data with the bold font in the table is the better data result. The last digit is the result of rounding. All data are the result of training 200 epochs. Figure 8: Precision-recall curve and ROC curve experimentally obtained from FAU-Net on the ELE data set from left to right. Figure 7: Precision-recall curve and ROC curve experimentally obtained from FAU-Net on the DRIVE data set from left to right. Figure 9: From left to right, the Precision-recall curve and ROC curve experimentally obtained on the CORO data set by FAU-Net. Table 1 compares the results of the original U-net, LadderNet, and U-net using the de-normalized structure on the DRIVE data set. AUC has improved. LadderNet, which uses a de-normalized structure, has achieved better results in both Precision and F\({}_{\beta}\)-score. Tables 2 and 3 compare the test results of U-net and LadderNet on the ELE and CORO data sets, respectively. Under the de-normalized structure, U-net's various indicators under the ELE data set have improved and achieved better performance. On the CORO data set, the results of using the de-normalized u-net and the original u-net are not large, and the original LadderNet achieves better performance on F\({}_{\beta}\)-score and AUC. According to the results of these experiments, better segmentation accuracy can be obtained using the improved de-normalization method. Table 4 compares the experimental results of our network with the current state-of-the-art network for vascular image segmentation on three data sets. On two data sets, DRIVE and ELE, our network has made significant improvements to Accuracy, F\({}_{\beta}\)-score, and AUC. These indicators are more critical for vascular segmentation. On the CORO data set, our network also has significant improvements in the two indicators, Accuracy and AUC. The area under our network-trained ROC curve and the precision-recall curve has achieved Accuracy above 0.9 on all three data sets, which shows that our model is more efficient and robust. Table 5 shows the calculation time for the test phase of each sample. We compared the U-Net, Recurrent U-Net, Residual U-Net, LadderNet, Dynamic Deep Networks, IterNet(Patched), R2U-Net and FAU-Net on the three blood vessel data set DRIVE and the brain blood vessel data sets ELE and CORO, respectively. Results proved that our model could achieve high accuracy under high efficiency. Taking a comprehensive look at Tables 1-3 and 5, we can see that using this de-normalized structure not only can get good experimental results but also can significantly reduce the computational time for testing. It is significant for clinical applications.