## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.5.2 Comparison of Visual Segmentation Results on DIARETDB1 Dataset  
In this paper, the DIARETDB1 dataset is utilized for a complementary validation of the performance of MLNet and compared with U-Net [15], Swin-UNet [40], and [PERSON] et al. [11]; the data results of these algorithms are from the relevant paper. The results are shown in Table 2.  
According to Table 2, MLNet achieved the highest scores on the DIARETDB1 dataset for both MA and SE types of lesions, achieving an AUPR of 53.01% and 65.19%, respectively, and improved by 6.42% and 1.76% relative to the suboptimal results. However, for the segmentation results of HE and EX types of lesions, the results obtained by MLNet were 73.81% and 76.66%, which were inferior to the 76.30% and 81.15% obtained by the algorithm proposed by reference [11]. Nevertheless, it is still more advantageous than the results of U-Net and Swin-UNet. In addition, for the average results, the proposed algorithm can achieve a significant improvement compared with U-Net and Swin-UNet, which are 6.95% and 9.28%, respectively, and a slight improvement of 0.25% compared with the algorithm in reference [11]. In addition to the analysis of AUPR values on the DIARETDB1 dataset, we also compare Dice indicators. As can be seen from Table 2, for the segmentation results of lesions of HE and EX types, the method proposed in this paper is inferior to that proposed by [PERSON] et al. [11]. In addition, for mDice, we have a decrease of 2.59% compared with method in reference [11]. However, compared with the compared algorithms, MLNet can still obtain suboptimal results on HE- and EX-type lesions and mDice. Regarding the performance metrics of MA and SE types of lesions, the proposed algorithm achieved the highest scores of 51.72% and 64.48%, respectively. While MLNet did not obtain the highest Dice scores for certain lesion types, it still demonstrated relatively excellent results for other lesion types. Moreover, MLNet exhibited a commendable overall performance score when considering the average results. It is worth noting that these results highlight the algorithm's competence in lesion segmentation.  
In general, the results of MLNet on the DIARETDB1 dataset complementarily verify that MLNet has good segmentation performance for fundus images.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**AUPR/\%**} & \multicolumn{6}{c}{**Dice/\%**} \\ \cline{2-10}  & **MA** & **SE** & **HE** & **EX** & **mAUPR** & **MA** & **SE** & **HE** & **EX** & **mDice** \\ \hline U-Net [15] & 41.72 & 55.53 & 67.11 & 76.47 & 60.21 & 44.53 & 52.26 & 58.01 & 69.18 & 56.99 \\ Swin-UNet [40] & 37.88 & 53.57 & 65.81 & 74.26 & 57.88 & 42.97 & 55.69 & 58.47 & 67.82 & 56.24 \\ [PERSON] et al. [11] & 46.77 & 63.43 & **76.30** & **81.15** & 66.91 & 49.96 & 62.23 & **69.31** & **76.12** & **64.41** \\ MLNet & **53.01** & **65.19** & 73.81 & 76.66 & **67.16** & **51.72** & **64.48** & 61.10 & 69.97 & 61.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics of different methods on the DIARETDB1 dataset.