## 5 Conclusions  
This paper presents MLNet, a DR lesion segmentation method based on multi-scale attention and lesion perception. MLNet mainly consists of the MSAB and the LPB, which effectively capture multi-scale DR lesion features and deeply perceive subtle lesions in retinal fundus images. Additionally, a novel loss function is designed to alleviate the negative impact of data imbalance, resulting in more accurate segmentation results. MLNet \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**AUPR/\%**} \\ \cline{2-6}  & **MA** & **SE** & **HE** & **EX** & **mAUPR** \\ \hline (No.4) Baseline + MSAB + LPB + Dice loss & 30.18 & 45.41 & 52.82 & 63.67 & 48.02 \\ (No.6) Baseline + MSAB + LPB + Tversky loss & 33.74 & 43.91 & 53.45 & 64.95 & 49.01 \\ (No.7) Baseline + MSAB + LPB + BCE loss & 27.12 & 39.64 & 44.38 & 54.13 & 41.32 \\ (No.8) Baseline + MSAB + LPB + Dice loss & 30.02 & 41.85 & 50.39 & 57.42 & 44.92 \\ (No.5) MLNet (Baseline + MSAB + LPB + Proposed loss) & **34.28** & **48.41** & **57.39** & **67.16** & **51.81** \\ \hline \hline \end{tabular} Bold values indicate the best-performing results.  
\end{table} 
Table 5: The loss function ablation experimental results of MLNet on the DDR dataset. is evaluated on the DDR dataset and compared with seven state-of-the-art methods, achieving mAUPR, mDice, and mIoU scores of 51.81%, 49.85%, and 37.19%, respectively, and achieving 67.16% mAUPR and 61.82% mDice on the DIARETDB1 dataset. These results show the superior performance of MLNet on the DDR dataset and good segmentation results on the DIARETDB1 dataset. MLNet also establishes excellent generalization on the IDRiD dataset, indicating its superior performance on the new dataset. In summary, MLNet improves the accuracy of DR lesion segmentation and has huge potential for future clinical practicality. It can serve as an assistive tool for ophthalmologists to accurately diagnose and treat patients with DR. It can also be used for training primary eye care practitioners to enhance their diagnostic abilities. Furthermore, MLNet can provide valuable insights for other types of lesion segmentation tasks, advancing research and development in the field. However, this work has certain limitations that can be addressed in future research. For example, this study does not employ any adaptive methods to address the issue of data imbalance in lesion segmentation. This means that researchers need to manually adjust weights and repeat experiments to obtain the optimal network parameters. While this approach allowed for some degree of optimization, it resulted in a longer training time and limited the network's compatibility with different datasets. To overcome this limitation, future research can focus on exploring adaptive methods specifically designed to handle imbalanced sample distributions in lesion segmentation. By developing techniques that automatically adjust for data imbalance, researchers can streamline the training process and improve the network's performance on diverse datasets. This would enhance the practicality and applicability of new DR image segmentation methods in real-world scenarios. Additionally, after providing fine-grained DR segmentation results, MLNet can be used to assist with DR classification tasks. By leveraging the segmentation outcomes, MLNet can contribute to more accurate DR diagnosis, aiding in the identification of disease severity and informing appropriate treatment strategies. Combining segmentation results with classification tasks has the potential to enhance overall diagnostic accuracy and improve patient management. Finally, the presentation of these limitations offers exciting prospects for future research. Proposing adaptive techniques to address data imbalance in lesion segmentation would shorten training time as well as improve the compatibility of deep learning-based methods across various datasets.