# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Experimental Results
In the comparative results, most methods only provide results for the mAP and AUC_ROC of four types of DR lesions. Therefore, we mainly  
Figure 7: The ROC and PR curves on IDRID of DRNet and our network. discuss the results of these two evaluation metrics for different methods on this dataset. For the segmentation of EX and MA, the mAP (60.51% and 14.35%) and AUC_ROC (97.74% and 95.64%) of our model are the best among all models; the mAP of HE segmentation is 0.84% higher than the second-ranked UNet [35]. Regarding the segmentation of SE, the AUC_ROC is significantly higher by 8.18% compared to RTNet [37]. The ROC curves and PR curves on DDR of our network are shown in Figure 8.  
Figure 8: The ROC and PR curves on DDR of our network. Table 3 shows the quantity analysis results on the ultra-wide-field fundus images. For the segmentation of EX, the mAP, F1, recall, and other metrics of LFRC-Net are the best among all the compared methods; the mAP is 57.90%, F1 is 52.77%, and recall is 41.95%. Regarding HE segmentation, our approach achieves the best AUC_ROC, mAP, and recall and the second best F1 (2.27% lower than SA_Unet [40]). The ROC curves and PR curves on this dataset of different models are shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Type of Lesion** & **AUC\_ROC** & **AUC\_PR** & **F1** & **Accuracy** & **Recall** & **Specificity** & **Precision** \\ \hline [PERSON] et al. [28] & EX & - & 50.68\% & - & - & - & - & - \\ \hline \multirow{3}{*}{HED [33]} & EX & 96.12\% & 42.52\% & - & - & - & - & - \\  & HE & 88.78\% & 20.14\% & - & - & - & - & - \\  & MA & 92.99\% & 6.52\% & - & - & - & - & - \\  & SE & 82.15\% & 13.01\% & - & - & - & - & - \\ \hline \multirow{3}{*}{DeepLab v3+ [34]} & EX & 96.41\% & 54.05\% & - & - & - & - & - \\  & HE & 93.08\% & 37.89\% & - & - & - & - & - \\  & MA & 92.45\% & 3.16\% & - & - & - & - & - \\  & SE & 86.42\% & 21.85\% & - & - & - & - & - \\ \hline \multirow{3}{*}{U-Net [35]} & EX & 97.41\% & 55.05\% & - & - & - & - & - \\  & HE & 93.87\% & 38.99\% & - & - & - & - & - \\  & MA & 93.66\% & 3.34\% & - & - & - & - & - \\  & SE & 87.78\% & 24.55\% & - & - & - & - & - \\ \hline \multirow{3}{*}{L-seg [36]} & EX & 97.26\% & 56.45\% & - & - & - & - & - \\  & HE & 92.98\% & 35.88\% & - & - & - & - & - \\  & MA & 94.23\% & 11.74\% & - & - & - & - & - \\  & SE & 87.95\% & 26.54\% & - & - & - & - & - \\ \hline \multirow{3}{*}{RTNet [37]} & EX & 97.51\% & 56.71\% & - & - & - & - & - \\  & HA & 93.21\% & 36.56\% & - & - & - & - & - \\  & MA & 94.52\% & 11.76\% & - & - & - & - & - \\  & SE & 88.45\% & 29.43\% & - & - & - & - & - \\ \hline \multirow{3}{*}{DR-Net [38]} & EX & - & - & - & 95.24\% & - & - & - \\  & HA & - & - & - & 96.91\% & - & - & - \\  & SE & - & - & - & 94.83\% & - & - & - \\ \hline \multirow{3}{*}{[PERSON] et al. [29]} & EX & 97.45\% & 56.07\% & - & - & - & - & - \\  & HE & **94.03**\% & 37.78\% & - & - & - & - & - \\  & MA & 94.09\% & 10.66\% & - & - & - & - & - \\  & SE & 88.57\% & 26.94\% & - & - & - & - & - \\ \hline \multirow{3}{*}{[PERSON] et al. [39]} & EX & - & 60.14\% & 54.06\% & - & - & - & - \\  & HE & - & 35.52\% & 33.17\% & - & - & - & - \\  & MA & - & 11.94\% & 15.22\% & - & - & - & - \\  & SE & - & **32.60\%** & **38.76\%** & - & - & - & - \\ \hline \multirow{3}{*}{Ours} & EX & **97.74\%** & **60.51\%** & **92.94\%** & **99.62\%** & **34.75\%** & 99.97\% & **86.24\%** \\  & HE & 91.78\% & **39.83\%** & 30.22\% & **99.14\%** & **12.01\%** & 99.98\% & **87.17\%** \\  & MA & **95.64\%** & **14.35\%** & **17.64\%** & **99.97\%** & **12.49\%** & 99.99\% & **40.36\%** \\ \cline{1-1}  & SE & **96.63\%** & 18.99\% & 25.24\% & **99.95\%** & **17.41\%** & **99.99\%** & **45.83\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative segmentation of EX, HE, MA, and SE on DDR.