## 3 Results  
All the models were trained under the conditions described above and tested first with an independent set consisting of 75 samples from healthy subjects and 73 samples from glaucoma subjects (test set). The results corresponding to the best and worst performance, per architecture, in terms of balanced accuracy have been included in Table 1. Tables 2-4 show the results obtained by evaluating the different network models on REFUGE, Drishti-GS1, and Papila as test sets. Table 5 shows a ranking of the five models with the best-balanced accuracy in the classification of each dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Architecture** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B. Accuracy** & **F1 Score** \\ \hline \multirow{8}{*}{CNNs} & VGG19 & fold\_1 & 0.8767 & 0.9467 & 0.9122 & 0.9117 & 0.9078 \\  & VGG19 & fold\_4 & 0.9863 & 0.9467 & 0.9662 & 0.9665 & 0.9664 \\ \cline{2-7}  & ResNet50 & fold\_1 & 0.9315 & 0.9067 & 0.9189 & 0.9191 & 0.9189 \\  & ResNet50 & fold\_3 & 0.9863 & 0.9600 & 0.9730 & 0.9732 & 0.9730 \\ \cline{2-7}  & InceptionV3 & fold\_2 & 0.9452 & 0.8667 & 0.9054 & 0.9059 & 0.9079 \\  & InceptionV3 & fold\_3 & 0.9726 & 0.9067 & 0.9392 & 0.9396 & 0.9404 \\ \cline{2-7}  & Xception & fold\_5 & 0.9315 & 0.8133 & 0.8716 & 0.8724 & 0.8774 \\  & Xception & fold\_1 & 0.9452 & 0.9067 & 0.9257 & 0.9259 & 0.9262 \\ \hline \multirow{8}{*}{ViTs} & Original ViT & fold\_5 & 0.9178 & 0.8933 & 0.9054 & 0.9056 & 0.9054 \\  & Original ViT & fold\_4 & 0.9726 & 0.8667 & 0.9189 & 0.9196 & 0.9221 \\ \cline{2-7}  & Swin Base & fold\_5 & 0.9452 & 0.8667 & 0.9054 & 0.9059 & 0.9079 \\  & Swin Base & fold\_1 & 0.9863 & 0.9333 & 0.9595 & 0.9598 & 0.9600 \\ \cline{2-7}  & Swin Tiny & fold\_1 & 0.9315 & 0.8933 & 0.9122 & 0.9124 & 0.9128 \\  & Swin Tiny & fold\_2 & 0.9863 & 0.8933 & 0.9392 & 0.9398 & 0.9412 \\ \cline{2-7}  & Twins-PCPVT & fold\_3 & 0.9452 & 0.8800 & 0.9122 & 0.9126 & 0.9139 \\  & Twins-PCPVT & fold\_2 & 1.0000 & 0.9333 & 0.9662 & 0.9667 & 0.9669 \\ \hline \multirow{8}{*}{Hybrid models} & DeiT & fold\_5 & 0.9178 & 0.9200 & 0.9189 & 0.9189 & 0.9178 \\  & DeiT & fold\_4 & 0.9863 & 0.9600 & 0.9730 & 0.9732 & 0.9730 \\ \cline{2-7}  & CaiT\_XXS36 & fold\_1 & 0.9041 & 0.8800 & 0.8919 & 0.8921 & 0.8919 \\ \cline{2-7}  & CaiT\_XXS36 & fold\_4 & 0.9315 & 0.9333 & 0.9324 & 0.9324 & 0.9315 \\ \cline{2-7}  & CaiT\_S24 & fold\_2 & 0.9178 & 0.8800 & 0.8986 & 0.8989 & 0.8993 \\ \cline{2-7}  & CaiT\_S24 & fold\_4 & 0.9178 & 0.9333 & 0.9257 & 0.9256 & 0.9241 \\ \cline{2-7}  & CeiT & fold\_3 & 0.9041 & 0.8667 & 0.8851 & 0.8854 & 0.8859 \\  & CeiT & fold\_2 & 0.9863 & 0.9067 & 0.9459 & 0.9465 & 0.9474 \\ \cline{2-7}  & ConViT & fold\_3 & 0.9589 & 0.8933 & 0.9257 & 0.9261 & 0.9272 \\  & ConViT & fold\_5 & 0.9863 & 0.9333 & 0.9595 & 0.9598 & 0.9600 \\ \hline \multirow{8}{*}{Others inspired} & ResMLP\_12 & fold\_3 & 0.9315 & 0.8533 & 0.8919 & 0.8924 & 0.8947 \\  & ResMLP\_12 & fold\_1 & 0.9452 & 0.9067 & 0.9257 & 0.9259 & 0.9262 \\ \cline{2-7}  & ResMLP\_B24 & fold\_1 & 0.9452 & 0.9467 & 0.9459 & 0.9459 & 0.9452 \\ \cline{2-7}  & ResMLP\_B24 & fold\_5 & 0.9863 & 0.9467 & 0.9662 & 0.9665 & 0.9664 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the results obtained by evaluating the different DL models on the test dataset according to different metrics. For simplicity, only the results corresponding to the folds with minimum and maximum balanced accuracy (B. Accuracy) are displayed.