# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Experimental Results  
Table 1 gives the quantity analysis results on IDRID. The ROC and PR curves on IDRID given by DRNet, FRCU-Net, and our network are illustrated in Figure 7. Results that are the best are indicated in bold black. The results of [22; 23; 24; 25; 26; 27; 28; 29; 30; 31] are from the original papers, while DRNet [32] and FRCU-Net [14] are given by our own implementations on this dataset. Our method achieve the highest AUC_ROC of HE and SE among all methods. The AUC_ROC of MA is lower at 0.51% than DRNet [32]. Additionally, the mAP of both EX and SE of our method rank first among all methods. The mAP for EX is 9.26% higher than [31] and 0.46% higher than [29]. We have the highest accuracy and specificity for the segmentation of the four types of DR lesions, which improves segmentation performance. Compared to the second-ranked [29], the mAPs of EX and SE increase by 0.46% and 0.41% respectively. At the same time, the F1 score of EX ranks second among all methods, only 0.06% lower than [22]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Type of Lesion** & **AUC\_ROC** & **mAP** & **F1** & **Accuracy** & **Recall** & **Specificity** & **Precision** \\ \hline [PERSON] et al. [22] & EX & - & 78.26\% & **78.15\%** & - & - & - & - \\ \hline [PERSON] et al. [23] & EX & - & - & - & 97.95\% & **96.38\%** & 97.14\% & - \\ \hline [PERSON] et al. [24] & EX & - & - & - & 97.21\% & 70.21\% & 92.09\% & - \\ \hline [PERSON] et al. [25] & EX & 98.93\% & 83.15\% & 76.81\% & 99.30\% & - & 99.74\% & - \\ \hline [PERSON] et al. [28] & EX & - & 77.67\% & - & - & - & - & - \\ \hline [PERSON] et al. [30] & EX & - & 85.00\% & - & - & 79.60\% & - & - \\ \hline [PERSON] et al. [26] & HE & - & - & - & 98.68\% & **80.49\%** & 99.68\% & 99.98\% \\ \hline [PERSON] et al. [31] & EX & - & 77.60\% & 71.10\% & - & - & - & - \\  & HE & - & 63.60\% & **59.40\%** & - & - & - & - \\  & MA & - & **53.20\%** & **43.80\%** & - & - & - & - \\ \hline \multirow{3}{*}{[PERSON] et al. [27]} & EX & - & - & 60.80\% & - & - & - & - \\  & HE & - & - & 45.76\% & - & - & - & - \\  & MA & - & - & **42.98\%** & - & - & - & - \\  & SE & - & - & 43.98\% & - & - & - & - \\ \hline \multirow{3}{*}{FRCU-Net [14]} & EX & 98.98\% & 84.66\% & 74.70\% & 99.30\% & 66.30\% & 99.82\% & 85.54\% \\  & HE & 95.69\% & 58.09\% & 45.35\% & 98.81\% & 48.83\% & 99.94\% & 68.73\% \\  & MA & 98.01\% & 38.64\% & 30.36\% & **99.87\%** & 21.55\% & 99.96\% & 56.01\% \\  & SE & 97.75\% & 55.79\% & 46.98\% & 98.88\% & 38.72\% & 99.80\% & 75.18\% \\ \hline \multirow{3}{*}{DRNet [32]} & EX & 99.00\% & 84.20\% & 73.82\% & 99.31\% & 64.30\% & 99.97\% & **86.64\%** \\  & HE & 95.81\% & 65.82\% & 53.80\% & 98.97\% & 99.85\% & 99.78\% & **83.96\%** \\ \cline{1-1}  & MA & **98.45\%** & 42.72\% & 36.91\% & 99.87\% & 25.79\% & **99.98\%** & **64.89\%** \\ \cline{1-1}  & SE & 98.64\% & 55.60\% & **48.80\%** & 99.80\% & **37.42\%** & 99.96\% & 70.12\% \\ \hline \multirow{3}{*}{[PERSON] et al. [29]} & EX & **99.43\%** & 86.40\% & - & - & - & - \\  & HE & 94.87\% & 64.94\% & - & - & - & - & - \\ \cline{1-1}  & MA & 98.39\% & 40.63\% & - & - & - & - & - \\ \cline{1-1}  & SE & 94.63\% & 59.78\% & - & - & - & - & - \\ \hline \multirow{3}{*}{Ours} & EX & 99.24\% & **86.86\%** & 78.09\% & **99.37\%** & 71.97\% & **99.81\%** & 85.34\% \\ \cline{1-1}  & HE & **96.34\%** & 62.97\% & 55.85\% & **96.95\%** & 44.05\% & 99.79\% & 76.27\% \\ \cline{1-1}  & MA & 97.94\% & 35.67\% & 28.75\% & **99.87\%** & **28.73\%** & 99.98\% & 61.81\% \\ \cline{1-1}  & SE & **98.86\%** & **60.19\%** & 47.31\% & **99.80\%** & 34.48\% & **99.97\%** & **75.36\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative segmentation of EX, HE, MA, and SE on IDRID. Table 2 presents the results of the segmentation on DDR. This paper contrasts the segmentation results of four DR lesions on DDR. The experimental results on HED [33], DeepLab v3+ [34], U-Net [35], and L-seg [36] come from RTNet [37], while [28; 29; 37; 38; 39] are based on their own publications.