## 3 Experimental Results
For RIM-ONE dataset, SqueezeNet model achieved the best result with the accuracy of 97.37% and AUC of 100%, followed by DenseNet model with the accuracy of 94.74% and AUC of 99.04%. For DRISTI-GS1 dataset, ShuffleNet model is superior compared to other deep CNN models with an accuracy of 86.67%, but AlexNet model achieved the best performance in term of AUC with the result of 81.48%. Table 6 shows the performance of the method P2 obtained from a SVM classifier using deep-activated features of eleven different pretrained CNNs across the datasets. Deep-activated features from DenseNet model provided the best performance for ACRIMA datasets with an accuracy of 96.23% and AUC of 98.75%, followed by the SqueezeNet model with the accuracy of 94.81% and AUC of 98.45%. For REFUGE dataset, DenseNet, ShuffleNet and ResNet-50 models achieved similar best performance, respectively with the accuracy of 93.50%, 93.75% and 94.75% and AUC of 93.94%, 92.24% and 90.66%. For DRISTI-GS1 dataset, InceptionResNet achieved the best results with the accuracy of 83.33% and AUC of 91.53% and achieved similar best performance with InceptionV3 model for ORIGA dataset with the accuracy of 78.46% and AUC of 82.06%, while InceptionV3 achieved the accuracy of 78.97% and AUC of 81.39%. For RIM-ONE dataset, ShuffleNet model achieved the best performance with an accuracy of 89.47% and AUC of 97.44%, followed by MobileNet model which obtained the accuracy of 86.84% and AUC of 94.23%. Table 7 illustrates the performance of the ensemble built by combining methods in each proposal P1 and P2 using average probabilities. The experimental results indicated that E(P1) achieved the best performance for RIM-ONE, ACRIMA and ORIGA datasets with the accuracy of 97.37%, 99.53% and 83.59% and AUCs of 100%, 99.98% and 88.86%, respectively, and achieved similar best performance with E(P2) for REFUGE dataset with the accuracy of 95.50% and AUC of 95.10%, while E(P2) achieved the accuracy of 95.75% and AUC of 94.32%. The E(P2) achieved the best performance for DRISTI-GS1 dataset with the accuracy of 90.00% and AUC of 92.06%. Tables 5-7 show that the results obtained from ACRIMA dataset are superior compared to other datasets and the ensemble methods, E(P1) and E(P2), perform well to find the optimal combination of the predictions managed to achieve the best performance for all five datasets with the average computational time 40 s and 53 s per image, respectively for E(P1) and E(P2). To evaluate ensemble methods E(P1) and E(P2) visually, we plotted ROC curves based on the relationship of the true positive and false positive rates for all five different datasets as demonstrated in Figure 10. The graphs visualize that the closer such a curve is to the top-left corner, the better results of the methods are. As an example of using ACRIMA dataset, the AUC value of E(P1) achieve better than as of E(P2) is the same results as of ROC curves illustrated in Figure 10b, the blue curve E(P1) is closer to the top-left corner comparing to that of orange curve E(P2). The same way for other four datasets as showed in Table 5, ROC curves of E(P1) are closer to the top-left corner comparing to that of E(P2) for REFUGE, RIM-ONE and ORIGA datasets, while ROC curve of E(P2) is closer to the top-left corner comparing to that of E(P1) for DRISTI-GS1 dataset.