## 3 Results  
In the first step of our experiment, we removed the blood vessel and optic disc by applying a heuristic method of a blood vessel and optic disc segmentation. The result of this process is illustrated in Figure 12. Images in Figure 12 illustrate the process of pre-processing steps. Figure 12 illustrates the results of each process, including those of the localization process, segmentation of OD, and segmentation of blood vessels. As illustrated in Figure 12, our localization method is suitable to recognize the location of the OD candidate. The result of localization is depicted with a red rectangle in Figure 12. For the overall localization process, our proposed method successfully achieved an accuracy of 100%, which means that our proposed method can precisely recognize all OD. After finding the location of OD, we conducted the OD segmentation. The result of this process can be seen in the step of OD segmentation shown in Figure 12. This result illustrates that the segmented optic disc seems close to the OD in the original images. Since the ORIGA-light dataset also provided an optic disc ground truth, then we evaluated our segmentation method and achieved an accuracy of 93.61%. According to the optic disc segmentation result that appeared in Figure 12 and achievement accuracy, we concluded that our proposed OD segmentation method was powerful enough to find and segment the optic disc area. After the OD segmentation was accomplished, the process was continued with blood vessel segmentation. In the next step, we conducted a blood vessel segmentation process by using a heuristic method, as described in Section 2. This process was started by multiplying the segmented OD with the original image, then, it was followed by extracting the green channel extraction. As our goal was that RNFL should become the major object to be analyzed, the green channel was chosen, because RNFL seemed clearer than RGB. The result of the blood vessel segmentation can be seen in the step of blood vessel segmentation shown in Figure 12. Since ORIGA-light did not provide the ground truth of blood vessels, we evaluated our segmentation process by analyzing the result visually. However, as depicted in Figure 12, almost the entire blood vessel area is successfully segmented. The last picture in Figure 12 illustrates the final result of the pre-processing step, which was the fundus image without the optic disc and blood vessel. This picture then became the input image of the deep learning process. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Network [Ref.]** & **Depth** & **Size (MB)** & **Parameters** & **Input Size** \\ \hline AlexNet [55] & 8 & 227 & 58,266,306 & 227 \(\times\) 227 \\ GoogleNet [56] & 22 & 27 & 1,252,072 & 224 \(\times\) 224 \\ Inception V3 [57] & 48 & 89 & 23,855,837 & 299 \(\times\) 299 \\ XceptionNet [58] & 71 & 85 & 20,865,578 & 299 \(\times\) 299 \\ ResNet-50 [59] & 107 & 98 & 23,591,810 & 224 \(\times\) 224 \\ InceptionResNet [60] & 164 & 209 & 26,530,290 & 299 \(\times\) 299 \\ MobileNet [61] & 53 & 13 & 4,256,893 & 224 \(\times\) 224 \\ NasNet [62] & \(\star\) & 360 & 5,329,777 & 331 \(\times\) 331 \\ DenseNet [63] & 201 & 77 & 7,039,554 & 224 \(\times\) 224 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Configuration and properties of each deep learning pre-trained model. In the next step, we performed a data augmentation process to alleviate the data imbalance problem. Before that, the dataset was then separated into training and testing parts, with a proportion of 80% (520) of data set up as training data and 20% (130) of data set up as testing data. To reach this goal, we applied the SMOTE filter function to the training data and achieved balanced training data of 1460 images. We utilized this training data on nine deep learning architectures using a transfer learning pre-trained model, as outlined in Table 3. On each of the training processes, the images were resized to satisfy the pixel dimension required by the corresponding pre-trained model.