## 4 Results and Discussion
### Pretrained Models for Classification  
The first step in the classification process is to test how well ResNet50, VGG19, DenseNet121, and EfficientNetB0 models work on the ORIGA, RIM-ONE DL, and HRF datasets, with different batch sizes. Several experiments were conducted on the ORIGA [16], HRF [17], and RIM-ONE DL [18] datasets. The ORIGA dataset was trained for 15 epochs with a batch size of 8, where metrics are shown in Table 18. The RIM-ONE DL dataset experiment followed a similar approach, with results detailed in Table 19. In the HRF dataset, initially comprising 30 images, models were trained and tested on an augmented dataset. Data augmentation generated eight images for each original one. Performance was evaluated based on several metrics provided in Table 20. The performance of four pre-trained models for glaucoma detection was evaluated, with EfficientNetB0 outperforming DenseNet121, VGG19, and ResNet50 across all metrics and datasets. Experiments 5.1, 5.2, and 5.3 evaluated EfficientNetB0 on ORIGA, RIM-ONE DL, and HRF datasets, varying batch sizes from 4 to 64, as indicated in Tables 21-23, respectively. This study found optimal performance with batch sizes of 8 for ORIGA and RIM-ONE DL and 4 and 8 for HRF, but performance declined with larger batch sizes. Early stopping was used to prevent overfitting and optimize resource use. Figure 10: The results of the augmentation techniques that applied to the segmented images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & \multicolumn{4}{c}{**Pretrained Models**} \\
**Metrics** & **EfficientNetB0** & **DenseNet121** & **VGG19** & **ResNet50** \\ \hline Train accuracy & 0.97 & 0.88 & 0.8 & 0.59 \\ Test accuracy & 0.9 & 0.82 & 0.88 & 0.63 \\ AUC & 0.9 & 0.82 & 0.88 & 0.63 \\ Precision & 0.9 & 0.87 & 0.89 & 0.63 \\ Recall & 0.9 & 0.82 & 0.88 & 0.63 \\ F1 score & 0.9 & 0.82 & 0.88 & 0.63 \\ Specificity & 0.92 & 1 & 0.96 & 0.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Results for each of the pretrained models trained on the ORIGA dataset. Figure 9: The results of the segmentation and the extracted ROI on the HRF dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & \multicolumn{5}{c}{**Pretrained Models**} \\
**Metrics** & **EfficientNetB0** & **DenseNet121** & **VGG19** & **ResNet50** \\ \hline Train accuracy & 0.98 & 0.97 & 0.92 & 0.51 \\ Test accuracy & 0.92 & 0.92 & 0.81 & 0.50 \\ AUC & 0.92 & 0.92 & 0.81 & 0.50 \\ Precision & 0.93 & 0.93 & 0.86 & 0.25 \\ Recall & 0.92 & 0.92 & 0.81 & 0.50 \\ F1 score & 0.92 & 0.92 & 0.80 & 0.33 \\ Specificity & 0.88 & 0.88 & 1.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Results for each of the pretrained models trained on the RIM-ONE DL dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Batch Size** & **Train Accuracy** & **Test Accuracy** & **AUC** & **Precision** & **Recall** & **F1 Score** & **Specificity** \\ \hline
8 & 0.97 & 0.90 & 0.90 & 0.90 & 0.90 & 0.92 \\
16 & 0.98 & 0.49 & 0.50 & 0.25 & 0.50 & 0.33 & 0.0 \\
32 & 0.99 & 0.53 & 0.54 & 0.76 & 0.54 & 0.41 & 0.08 \\
64 & 0.99 & 0.51 & 0.52 & 0.75 & 0.52 & 0.37 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Results for each of the pretrained models trained on the augmented HRF dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Performance** & \multicolumn{5}{c}{**Pretrained Models**} \\
**Metrics** & **EfficientNetB0** & **DenseNet121** & **VGG19** & **ResNet50** \\ \hline Train accuracy & 0.98 & 0.97 & 0.90 & 0.59 \\ Test accuracy & 1.00 & 0.62 & 0.38 & 0.50 \\ AUC & 1.00 & 0.63 & 0.38 & 0.50 \\ Precision & 1.00 & 0.62 & 0.37 & 0.50 \\ Recall & 1.00 & 0.62 & 0.38 & 0.50 \\ F1 score & 1.00 & 0.56 & 0.37 & 0.50 \\ Specificity & 1.00 & 1.00 & 0.50 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 21: The performance metrics values with different batch sizes in Experiment 5.1.