## 4 Experimental Results
### DME Grading Module Results  
We employed the Risk of Macular Edema data from the Messidor dataset to evaluate our results. Table 7 displays the performance metrics obtained from three experiments conducted with different preprocessing methods: No preprocessing (Original), Contrast Enhancement (CE), and CIELAB preprocessing of images. Majority voting is used to calculate metric values, especially in ensemble methods. In this approach, multiple models are trained on the same dataset, and their predictions are combined to make a final decision. This method has benefits, such as improving prediction accuracy by leveraging the strengths  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **1/2R** & **1R** & **2R** \\ \hline [PERSON] [43] & 91.28 & 98.24 & 99.56 \\ [PERSON] [44] & 96.08 & 98.58 & 99.50 \\ Our Method & 79.40 & 99.38 & 99.91 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of macula detection scores in percentage.  
Figure 9: Images whose macula localization satisfies the 1R and \(1/2\)R criterion. The circles indicate the detected macular regions and squares indicate the ODs.  
of multiple models and reducing the impact of noise or incorrect predictions from individual models.  
Figure 10 presents examples of the grading results obtained with the proposed system for each case.  
Finally, a comparison with other non-interpretable and interpretable methods is presented in Table 8.  
Figure 10: Results of HaEx enhancement. The circles indicate the detected macular regions and squares indicate the ODs. HaExs within the macular region are depicted in pink and outside are depicted in green.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Value** \\ \hline Accuracy & 91.12\% \\ Precision & 91.31\% \\ Recall & 91.12\% \\ F1-Score & 90.72\% \\ Specificity & 93.00\% \\ Sensitivity & 91.12\% \\ AUC & 0.9334 \\ \hline \hline \end{tabular}
\end{table}
Table 7: DME grading metrics results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multicolumn{4}{c}{Non-interpretable methods} \\ [PERSON] [12] & 88.8\% & 74.4\% & 96.5\% \\ [PERSON] [18] & 95.47\% & 94.68\% & 97.19\% \\ [PERSON] [5] & 97.06\% & - & - \\ [PERSON] [20] & 98.66\% & 98.66\% & 99.32\% \\ \multicolumn{4}{c}{Partially Interpretable Methods} \\ [PERSON] [14] & 96.33\% & - & - \\ [PERSON] [17] & 86.3\% & 79.5\% & 87.7\% \\ \multicolumn{4}{c}{Totally Interpretable Method} \\ Our Method & 91.12\% & 91.12\% & 93.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of the proposed method with other non-interpretable and interpretable DME grading methods on MESSIDOR.  
The results show that non-interpretable methods generally perform better in accuracy, sensitivity, and specificity. While our method shows slightly lower accuracy than some of the non-interpretable methods, it maintains balanced sensitivity and specificity, suggesting a robust performance in DME grading. However, interpretable methods like ours demonstrate competitive performance while providing insights into the modules, which may be advantageous in certain contexts, such as clinical decision-making or model interpretability.