## 4 Experimental Results  
The experiments were implemented on two Nvidia Quadro RTX 8000 GPUs in an Ubuntu environment. Fold validation was implemented to obtain more robust results, and due to the size of the dataset, we used five-fold cross-validation to train on 80% of the dataset and tested using 20% of the original dataset during each trial. Early stopping callback was used to minimize validation loss.  
Models were evaluated using accuracy precision recall and F1 score; the equations used were applied for the performance assessment mentioned in [36; 37; 38].  
Figure 5: Fuzzy rules developed by the human expert sample.  
Figure 6: The membership functions for the diabetic retinopathy (DR) stage output variable.  
Table 6 shows that DRSM-CNNFIS outperforms the CNN-only models--DenseNet-201, Inception-ResNet-V2, Inception-V3, ResNet-50, Xception, Majority Vote Ensemble, and Average Ensemble--with an accuracy of 0.9281. The model was evaluated on the macro average, the weighted average for precision, recall, and F1-score to obtain the performance of the model using the single-label classification method of the study. The macro average and the weighted average for precision, recall, and F1-score were evaluated for five classes. Macro averages of 0.7142, 0.7753, and 0  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline Class 0—No Diabetic & 0.9326 & 0.8990 & 0.9155 \\ Retinopathy & 0.3628 & 0.4061 & 0.3832 \\ Class 2—Moderate & 0.6177 & 0.6083 & 0.6130 \\ Class 3—Severe & 0.3914 & 0.6795 & 0.4967 \\ Class 4—Proliferative & 0.5343 & 0.6006 & 0.5655 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Experiment 5—Xception CNN model class-specific metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline Class 0—No Diabetic & 0.9312 & 0.9379 & 0.9345 \\ \hline Class 1—Mild & 0.3983 & 0.4701 & 0.4312 \\ Class 2—Moderate & 0.7405 & 0.5554 & 0.6347 \\ Class 3—Severe & 0.4080 & 0.6750 & 0.5086 \\ Class 4—Proliferative & 0.5622 & 0.6226 & 0.5908 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Experiment 7—Majority Vote Ensemble class-specific metrics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline Class 0—No Diabetic & 0.9326 & 0.8990 & 0.9155 \\ \hline Class 1—Mild & 0.3628 & 0.4061 & 0.3832 \\ Class 2—Moderate & 0.6177 & 0.6083 & 0.6130 \\ Class 3—Severe & 0.3914 & 0.6795 & 0.4967 \\ Class 4—Proliferative & 0.5343 & 0.6006 & 0.5655 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Experiment 6—Average Predictions Ensemble class-specific metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Precision** & **Recall** & **F1-Score** \\ \hline Class 0—No Diabetic & 0.9260 & 0.9166 & 0.9213 \\ Class 1—Mild & 0.3167 & 0.4751 & 0.3801 \\ Class 2—Moderate & 0.7303 & 0.4254 & 0.5376 \\ Class 3—Severe & 0.3453 & 0.7205 & 0.4669 \\ Class 4—Proliferative & 0.4658 & 0.6198 & 0.5319 \\ Diabetic Retinopathy & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experiment 3—Inception-V3 CNN model class-specific metrics.