## 4 Discussion  
An automated glaucoma diagnosis system is an essential task to save people's vision due to its value in assisting the ophthalmologists to screen the glaucoma disease in a faster and inexpensive way. However, the lack of a common standard for comparison makes the evaluated result is not necessarily best comparing to the previous methods due to the use of different databases. To ease the validation criteria, we experiment several methods in each dataset separately and compare all results of our proposed methods in this study as well as compare with the common works in the literature which used the same experimental data. 2787 retinal images from five datasets are used separately to evaluate the performance of the proposed methods. The results indicate that the top score of methods in P1 achieves better performance for REFUGE, RIM-ONE and ACRIMA datasets comparing with the top score of methods in P2, while the top score of methods in P2 is superior compared to the top score of methods in P1 for DRISTI-GS1 dataset. However, the top score of both methods achieves the similar performance using ORIGA dataset. To improve the performance of glaucoma detection, the ensemble method is applied. The result indicates that E(P1) and E(P2) are superior comparing to the top score of methods in P1 and P2, respectively for all five datasets. For overall, E(P1) achieves the best performance for RIM-ONE, ACRIMA and ORIGA datasets and achieve similar best performance with E(P2) for REFUGE dataset while E(P2) achieves the best performance for DRISTI-GS1 dataset. In addition, we also compare our results with the previous works as reported in Table 8. The results indicate that our methods, both E(P1) and E(P2), achieve better results comparing to the conventional methods for RIM-ONE, ORIGA, DRISTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90%, 86.84% and 99.53%, and AUC of 100%, 92.06%, 91.67% and 99.98%, respectively, and achieve the comparable results with CUHKMED, the top team in REFUGE challenging using REFUGE dataset with the accuracy of 95.59% and AUC of 95.10%. From the above study, we found that the studies in [15; 19; 20] used simpler machine learning to classify the features from the cropped OD images for glaucoma classification and achieved the accuracy of 76.77%, 81.32%, and 76.90%, respectively on different datasets. The obtained results were not really promising. Deep CNN models were applied in [6; 21; 22; 23] and they achieved better performance. However, those papers applied an individual CNN architecture using one or two datasets. It cannot be sure that their best CNN model will be good for another dataset. To address this matter, here we performed a comparative study from eleven CNN models using transfer learning technique in P1 and deep feature extraction technique along with SVM classifier P2 using Five datasets. [PERSON] et al. [5] presented the ensemble of CNN models and they achieved very good results using REFUGE dataset. Nevertheless, they used only a few CNN architectures with similar structure, four ResNet models, which did not efficiently explore the other potential features. In our study, we developed the ensemble of diverse CNN models using eleven CNN models with different architecture and each of them with different learning perspective. As all the methods presented in our study are tested and validated on five online available datasets, we believe that the obtained results will be valuable for future studies. Although the obtained results are promising, there are still rooms for improvement and limitations to be alleviated. One fundamental limitation rises from the datasets when trying to generalize. We found that the available datasets are different in the way they are captured from different ethics using different devices, the labeling criteria and the quality of the image. Since our study separately tested each dataset, each algorithm is trained on the dataset with same devices, race and similar appearance. One dataset rarely contains diversity of images from different device, race.