## 4 Discussion  
The results of Tables 1-4 clearly show that all architectures present the highest performance when classifying the images of the test set (Table 1). For this set, most of the trained models of each architecture have a balanced accuracy exceeding 90%. Such optimal results are not repeated when the set of retinographies to be classified changes. For example, in the case of Refuge, the best models classify with a balanced accuracy of around 83-89% (Table 2). In Drishti-GS1, the best results are in the range of 78-85% (Table 3), and in Papila, the most efficient models present a balanced accuracy of 74-80% (Table 4). These results are quite logical, considering that the test set belongs to the same set of images with which the DL models were trained, while the other three sets are different. Although these datasets are also composed of retinographies of healthy and glaucoma eyes, the images have been acquired with other cameras, under other conditions, and have been diagnosed by different medical experts. Another important factor to consider is that these external datasets are unbalanced, which is why we are using the balanced accuracy measure as a reference in our comparison. Table 5 shows a ranking of the five models with the best-balanced accuracy in the classification of each dataset. It is interesting to note that, for all the datasets, the best model found is some type of CNN. However, the difference in efficiency with the second-ranked models, where some hybrid methods already appear, is very small. There are no significant differences between the best CNNs and ViT-based models when classifying the different datasets considered. To study the variability of the five trained models of each architecture when performing the same classification task, we have plotted their balanced accuracy values in a Boxplot for each dataset: test set (Figure 15), Refuge dataset (Figure 16), Drishti-GS1 dataset (Figure 17), and Papila dataset (Figure 18). In the case of the test set classification (Figure 15), the architecture with the lowest efficiency variability is ResMLPB_24. Of the five ResMLPB_24 models, four have a balanced accuracy of around 94%. On the other hand, the architecture ResNet50, previously presented as the best for classifying the test set (ranked 1 st in Table 5), is the one with the highest balanced accuracy variability among its five trained models. Another interesting observation is that the VGG19 architecture is the one with the highest median value. This might lead us to think that this architecture may perform globally better for classifying the test set; however, it is the last to appear for this task in Table 5.  
With the Refuge dataset, the architecture with the lowest balanced accuracy variability continues to be ResMLPB_24 (Figure 16). There are other architectures that present low variability as well but with worse balanced accuracy values (e.g., DeiT and VGG19). ResMLPB_24 is also the architecture that presents the highest median value, but it is ranked 4 th in the ranking (Table 5). On the other hand, the CaiT_XXS36 architecture presents the highest variability with the worst efficiency values. It is interesting to note that by \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ranking Position** & **Test Set** & **Refuge** & **Drishti-GS1** & **Papila** \\ \hline \multirow{2}{*}{1} & ResNet50 & InceptionV3 & ResNet50 & VGG19 \\  & 97.32\% & 89.44\% & 85.14\% & 80.12\% \\ \hline \multirow{2}{*}{2} & DeiT & ConViT & CaiT\_XXS36 & InceptionV3 \\  & 97.32\% & 88.84\% & 83.02\% & 79.75\% \\ \hline \multirow{2}{*}{3} & Twins-PCPVT & VGG19 & InceptionV3 & ResMLPB\_24 \\  & 96.67\% & 88.66\% & 83.00\% & 79.62\% \\ \hline \multirow{2}{*}{4} & ResMLPB\_24 & ResMLPB\_24 & ResMLPB\_24 & ResNet50 \\  & 96.65\% & 88.56\% & 82.81\% & 79.03\% \\ \hline \multirow{2}{*}{5} & VGG19 & Original ViT & VGG19 & CaiT\_S24 \\  & 96.65\% & 87.55\% & 82.47\% & 78.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the top five models for each dataset and their values of balanced accuracy.