# Lightweight Frequency Recalibration Network for Diabetic Retinopathy Multi-Lesion Segmentation
## 3 Results and Discussion
### Ablation  
Ablation experiments are conducted to validate the effectiveness of the proposed RAM and FRCM on the IDRID, DDR, and ultra-wide-field fundus images datasets, which are presented separately in Tables 5-7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **U-Net** & **AttentionUnet** & **DRNet** & **CAR\_UNet** & **Ours** \\ \hline Parameter & 1.95 M & 2.34 M & 1.62 M & 1.05 M & 0.95 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the different lightweight model parameters.  
Figure 9: The ROC and PR curves on ultra-wide-field fundus images of different models.  
size, posing challenges for accurately locating the positions of MA and segmenting based on their shapes. In contrast to solely introducing the FRCM, simultaneously introducing RAM and the FRCM increases the F1 score of MA by 1.22%, which indicates that RAM can assist in accurately locating the positions of small lesions, thereby enhancing segmentation performance. Although the simultaneous introduction of RAM and the FRCM leads to a 2.48% decrease in the F1 score of SE, while the mAP increases by 12.16%. Such results suggest a significant improvement in prediction accuracy, making this performance loss acceptable. Furthermore, compared to the baseline network, the mAP of EX, HE, MA, and SE increases by 5.17%, 4.93%, 10.57%, and 21.45%, respectively, and the F1 also improves by 6.63%, 7.86%, 13.69%, and 17.27% after introducing these two modules, which demonstrates that the low-frequency domain is more effective in segmenting lesion areas and reducing the impact of texture information bias. Overall, our network achieves the highest AUC_ROC and mAP of EX and SE while the mAP and F1 of HE also reaches optimal levels, which indicates the beneficial effects of introducing RAM and the FRCM on enhancing lesion segmentation.  
For Table 6 on DDR, the baseline network exhibits relatively low performance with F1 of four DR lesions: 43.97%, 21.57%, 7.98% and 25.71%. After the introduction of RAM, the F1 of EX, MA, and SE increases by 2.34%, 0.32%, and 0.68% separately. After solely introducing the FRCM, the F1 score of HE decreases by 3.36%. This is because of the lower image quality of the DDR dataset and the influence of lighting conditions, leading to an incomplete presentation of the shape of HE lesions in some images, thereby affecting the segmentation effectiveness of HE. However, following the adoption of the FRCM, the AUC_ROC, mAP, and accuracy of the four types of DR lesions are all optimal. Additionally, the F1 scores of EX, MA, and SE are also the highest. The conclusion that both modules contribute to mitigating interference from background information and enhance the segmentation abilities of our network is obtained through these analyses.  
As is shown in Table 7, after introducing RAM, the AUC_ROC, mAP, and F1 scores for both EX and HE improve. Following the introduction of the FRCM and compared to solely introducing RAM, although the F1 score of EX decreases by 0.81%, the mAP increases by 5.47%. Such performance loss is deemed acceptable. Overall, with the introduction of both RAM and the FRCM, the mAP, accuracy, and recall for EX are optimal, and similarly for HE, the AUC_ROC, mAP, F1, accuracy, and recall are also the highest. This result reinforces the conclusion we mentioned above and demonstrates the effectiveness of our network in segmenting multiple DR lesions on different datasets.