## 5 Discussion  
As mentioned before, the main objective of the proposed system is to provide an interpretable DME grading system that can be used in any local region, including low-resource areas and in telemedicine frameworks. Considering this context, it is desirable for the proposed system to work on a mobile device or any common personal computer. The time elapsed from introducing an input retinal image to obtaining the DME grade is measured on a personal computer with an Intel(r) i7 processor and 16 GB RAM, obtaining  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **With the Sequence** & **Without the Sequence** \\ \hline Percentage of images & 100\% & 43.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ablation study about the impact of the sequence of preprocesses on macula area detection.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model with Preprocessing** & **Accuracy (\%)** & **AUC** \\ \hline ConvUNeXt-Original only & 80.98 & 0.9334 \\ ConvUNeXt-CE only & 78.86 & 0.8836 \\ ConvUNeXt-CIELAB only & 88.12 & 0.9069 \\ Majority Voting & 91.12 & 0.9334 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation study about the preprocessing effect on the DME grading.  
approximately 3.8 s per input image. This processing time may be short enough, but if multiple images are required per patient, the processing time must be reduced.  
Although the proposed system employs several image enhancement techniques to mitigate the impact of low-quality images on the accuracy of the grading, the relatively inexpensive ophthalmoscopies used in low-resource areas typically provide low-resolution images that may lead to misclassification of DME grading. Considering that the quality of some retinal images can be too low to diagnose, these low-quality images must be detected before their introduction to the proposed system because adapting the proposed algorithm to any low-quality retinal images is a very challenging task.  
Recently, optical coherence tomography (OCT) has been used to detect various eye diseases such as DME, macular holes, age-related macular degeneration, and so on. Applying deep learning techniques to images generated by spectral-domain OCT (SD-OCT) can result in a high DME detection rate of over 99% [37]. Undoubtedly, the combination of OCT technology and deep learning will become the main methodology for diagnosing DME. However, at present, OCT equipment is still expensive for any local hospital in developing countries compared to traditional ophthalmoscopies, making it difficult to use OCT.  
In segmentation tasks, constructing accurate GT masks is crucial during both the training and testing phases. As mentioned before, the shapes of the GT masks for Messidor and Retinal Lesions are very similar, allowing for proper training and testing. To evaluate possible bias produced by the private GT masks, we obtained Mean IoU using test images, without enhancement, from only the Retinal Lesions dataset. The obtained mean IoU is 66.4, which is 3 points lower than the mean IoU obtained from all test images (69.5) as shown in Table 4. In this sense, there is a small bias due to private GT masks or different types of retinal images used in Retinal Lesions.