## 3 Results
The results of our experiment are presented in Table 4. In Table 4, we also presented the result of the pre-trained model tested in the testing data of the ORIGA-Light dataset without any pre-treatment processes. In addition, we compared our work with a previous study that had similar experiment scenarios. Table 4 demonstrates the results of our experiments compared to two different approaches. The pre-trained model conducted by [64] was carried out by utilizing the original CNN pre-trained model without any additional treatment. They used the original cropped optic disc of the ORIGA-Light dataset as input images. According to Table 4, MobileNet performed the best in the ORIGA-Light dataset, with an accuracy of 80%, followed by InceptionResNet, with an accuracy of 90%, and XceptionNet and DenseNet, with similar \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Network [Ref.]**} & \multirow{2}{*}{**Input Size**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{3-5}  & & **[PERSON] et al. (2020)**[64] & \\ \cline{3-5}  & & **Pre-Trained Model** & **CNN+SVM** & \\ \hline AlexNet [55] & 227 \(\times\) 227 & 68.42 & 76.41 & 92.61 \\ GoogleNet [56] & 224 \(\times\) 224 & 71.79 & 74.87 & 87.95 \\ Inception V3 [57] & 299 \(\times\) 299 & 75.38 & 78.97 & 75.57 \\ XceptionNet [58] & 299 \(\times\) 299 & 77.44 & 75.9 & 71.76 \\ ResNet-50 [59] & 224 \(\times\) 224 & 75.9 & 75.38 & 84.93 \\ InceptionResNet [60] & 299 \(\times\) 299 & 80 & 78.46 & 74.05 \\ MobileNet [61] & 224 \(\times\) 224 & 80.51 & 74.36 & 75.57 \\ NasNet [62] & 331 \(\times\) 331 & 73.85 & 74.87 & 78.63 \\ DenseNet [63] & 224 \(\times\) 224 & 77.44 & 78.46 & 92.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results to a previous study that had similar scenarios. Figure 12: Result of a pre-processing step including localization step, OD segmentation, and blood vessel segmentation. accuracies of 77.44% each. Whilst, the CNN+SVM proposed by [PERSON] et al. [64] achieved an accuracy of less than 80% in all deep learning architecture. The highest performance of this method was carried out by Inception V3, with an accuracy of 78.97%, followed by InceptionResNet dan DenseNet, with similar accuracies of 78.46% each. Similar to the previous study, this method used cropped optic discs as input images in their deep learning model. Those results show that their proposed method was good enough in the ORIGA-Light dataset. Offering another solution, we proposed a different way to analyze glaucoma disease. We used the characteristic of RNFL as the major object to recognize the progression of glaucoma disease. In our experiments, almost all deep learning architectures achieved good enough performance. The highest accuracy was carried out by DenseNet, with an accuracy of 92.88%, followed by AlexNet, with an accuracy of 92.61%, and GoogleNet, with an accuracy of 87.95%. This result successfully improved by more than 15% from the two previous research works proposed by [64]. According to the results presented in Table 4, we might conclude that our proposed method was suitable enough to recognize glaucoma in the ORIGA-Light dataset with the highest accuracy, as compared to other models and previous studies techniques, with a more than 15% accuracy improvement. The confusion matrix shown in Figure 13 was provided to add more detailed information about the model performance. The confusion matrix depicted in Figure 13 was obtained by performing DenseNet. This result indicated that utilizing RNFL became a proper and effective solution to analyze the progression of glaucoma disease. In addition, Figure 14 is an example of the classification results predicted by DenseNet, using the image data preprocessed by our proposed approach. Figure 14a,b show the glaucomatous and normal eyes, respectively.