# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Visualization  
The visualization results of DRNet, FRCU-Net, and our network for four types of DR lesions on the IDRID dataset are shown in Figure 10. Red boxes denote the labelled lesion areas, while blue boxes indicate the segmentation results. The first and second rows display the segmentation of EX, the third and fourth rows for HE, the fifth and sixth rows for SE, and the seventh and eighth rows for MA. In the segmentation of EX, it is obvious that our network can segment lesion areas where DRNet [32] and FRCU-Net [14] fail. For HE, from the third and fourth rows, it can be seen that our network achieves more precise segmentation compared to other networks, reducing the possibility of mis-segmentation. For SE, our algorithm is also more robust and obtains the better segmentation results. This demonstrates that our network improves segmentation performance by simultaneously utilizing the shape and texture information of DR lesions, reducing interference from irrelevant features. As far as MA is concerned, there is not much difference between the three algorithms in terms of results, because MA occupies only a few pixels, which challenges the involved segmentation methods.  
For the visualization on the ultra-wide-field fundus images in Figure 11, the first and second rows show the segmentation results of EX and the third and fourth rows for HE. Green boxes represent lesion areas, and for clarity, red boxes provide magnified views of the green ones, while blue denote the segmentation results of the DR lesions. For EX, it is evident that our model accurately identifies the lesion areas. It can be observed in the third row that our network is also superior to other models even when the lesion areas are relatively scattered such as HE.  
Figure 10: Visualized results of LFRC-Net and DRNet on IDRID.  
Figures 12-14 demonstrate the segmentation results of our network on IDRID, DDR, and ultra-wide-field fundus image datasets separately. The first row shows the original images, the second row presents the ground truth, and the third row showcases the segmentation achieved by our model. In the segmentations depicted in Figures 12 and 13, our network simultaneously segments four DR lesions. Blue, red, green, and yellow represent the segmentation results of EX, HE, SE, and MA, respectively. Due to the inconsistent image quality and significant impact of lighting in the DDR dataset, its segmentation performance is not as good as that of the IDRID dataset.  
Figure 11: Visualized result of different models on ultra-wide-field fundus images.  
Figure 12: Visualized results of our LFRC-Net on IDRID.  
Figure 14 illustrates our network's segmentation on ultra-wide-field fundus images. Our network simultaneously segments both EX and HE with blue and red, respectively. The low contrast of these fundus images and the sparsity of lesions make the segmentation full of challenges. Furthermore, there is a lot of interference in the image, such as eyelids and eyelashes, while the region of interest is only a small part. Our approach achieves higher-precision segmentation and a more comprehensive delineation of regions by utilizing attention mechanisms despite these factors.  
Figure 14: Visualized results of our LFRC-Net on ultra-wide-field fundus images.  
Figure 13: Visualized results of our LFRC-Net on DDR.