## 4 Discussion
increasing the depth of this model and using its more complex version, CaiT_S24, both aspects improve notably. In the Drishi-GS1 dataset classification, the architecture with the lowest balanced accuracy variability is Xception, but its efficiency is not among the best. Taking both factors into account, perhaps VGG19 is more optimal since its variability is low and its balanced accuracy values are higher. Table 5 shows that the architecture with the model with the highest balanced accuracy value is ResNet50. This architecture, as shown in Figure 17, has a medium variability. The original ViT is the one with the highest variability in this classification problem. Figure 16: Boxplot of the balanced accuracy values of the five models of each architecture when classifying the Refuge dataset: minimum value, maximum value, median, and quartiles 1 and 3. Figure 15: Boxplot of the balanced accuracy values of the five models of each architecture when classifying the test set: minimum value, maximum value, median, and quartiles 1 and 3. The architecture with the lowest efficiency variability when classifying the Papila dataset is CaiT_XXS36, but its efficiency improves notably when, again, the deepest version of this model, CaiT_S24, is used (Figure 18). VGG19 and ResMLP_24 seem to behave globally in a similar way, with VGG19 being slightly superior. This coincides with what is recorded in the ranking presented in Table 5.  
One thing that occurs in all cases is that the ResMLP_12 architecture tends to present significantly lower balanced accuracy values and much higher variability than its deeper version, ResMLP_24, just as with the versions of CaiT and Swin selected. Therefore, it seems convenient to choose, within the same architecture, the deeper versions of the glaucoma problem addressed in this article. If we do not consider these simplified versions  
Figure 17: Boxplot of the balanced accuracy values of the five models of each architecture when classifying the Drishti-GS1 dataset: minimum value, maximum value, median, and quartiles 1 and 3. Figure 18: Boxplot of the balanced accuracy values of the five models of each architecture when classifying the Papila dataset: minimum value, maximum value, median, and quartiles 1 and 3.  
of the studied architectures, we can observe that CNNs and ViT-based architectures present a similar behavior when classifying retinographies. Whenever a comparative study such as the one proposed in this article is presented, it is always interesting to analyse the similarities and differences with other related works published in the scientific literature. In our case, we have not found any work that follows the experimental methodology we have developed, consisting of training models of 15 different architectures (between CNNs and ViT-based systems) with the retinographies of two databases (RIM_ONE DL and private data) and testing them with public databases not used in training (Refuge, Papila, and Drishti-GS1). In the Introduction Section, we have briefly described other related published works [14, 15, 16, 17, 35, 36, 39]. Some of them analyse and compare the efficiency of some architectures studied in our work, such as ResNet50 [15, 16, 36, 39], VGG19 [16, 36], Xception [36], ViT and Swin Transformer [35, 36], DeiT, ResMLP, and CaiT [35]. However, the training and test conditions are significantly different. On the other hand, there are works that propose their own architectures, as in [15, 17]. Regarding the datasets used in the training and testing stages, each work uses and combines them differently. Some datasets are public and match those used in our work, e.g., Drishti-GS1 [14, 15, 16, 35, 36], Rim-ONE DL or some of its earlier versions [16, 35, 36, 39], and Refuge [35, 36], but others are different and, in some cases, private (ACRIMA [14, 39, 14], ORIGA [14, 17, 35, 39], SCES [17], HVD [14], OHTS [39], HRF [15, 35], DRIONS-DB [15], LAG [35, 39], ESPERANZA [16], ODIR 5K [35]). Taking all this into account, we will now compare our results with those of these works indicatively.