## 4 Results and Discussion
### Pretrained Models for Classification
\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. \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 & 0.98 & 0.92 & 0.92 & 0.93 & 0.92 & 0.92 & 0.88 \\
16 & 0.99 & 0.90 & 0.90 & 0.90 & 0.90 & 0.90 & 0.92 \\
32 & 0.99 & 0.85 & 0.85 & 0.87 & 0.85 & 0.84 & 0.96 \\
64 & 0.99 & 0.77 & 0.77 & 0.84 & 0.77 & 0.76 & 1.0 \\ \hline \hline \endThe model was trained on ORIGA and RIM-ONE DL datasets with a batch size of 8, and on the HRF dataset with a batch size of 4. It performed best at 20 epochs for ORIGA, 15 epochs for RIM-ONE DL, and 7 epochs for HRF. However, for HRF, increasing epochs to 15 improved performances, but further increase to 20 epochs decreased performance, indicating no further improvement beyond 15 epochs. This study used a pretrained EfficientNetB0 model to assess the impact of data augmentation on image classification. Experiments 7.1, 7.2, and 7.3 applied random rotation, brightness adjustment, and horizontal flips to original images from the ORIGA, RIM-ONE DL, and HRF datasets, resulting in eight times more images for each dataset. Performance metrics for the ORIGA and RIM-ONE DL datasets, trained on augmented images with various batch sizes, are presented in Tables 27 and 28, respectively. Results show that EfficientNetB0 performed optimally on ORIGA with batch size 32 and on RIM-ONE DL with batch size 8. However, overfitting was observed in larger batch sizes, prompting early stopping to improve test accuracy. Experiments 8.1 and 8.2 assessed the impact of varying epochs on the performance of an EfficientNetB0 model trained on augmented ORIGA and RIM-ONE DL with batch sizes of 32 and 8, respectively. Tables 29 and 30 illustrate performance metrics for various epochs, providing insights into the model's classification accuracy under various conditions. The EfficientNetB0 model was tested on ORIGA, RIM-ONE DL, and HRF datasets, showing optimal performance at 15 epochs, but potential overfitting was observed beyond 15 epochs. Figures 11-13 display the corresponding training and validation loss, along with accuracy over 15 epochs for the ORIGA, RIM-ONE DL, and HRF datasets, respectively. In all cases, both curves decreased and converged, indicating effective learning and generalization. High accuracy on both sets suggests reliable real-world predictions. For HRF, the model's error decreased, and correct predictions increased over time, indicating improvement. Figure 14-16 display the true and predicted values on randomly selected images from the test datasets of ORIGA, RIM-ONE DL, and HRF, respectively. Figure 14 contains to 10 images from ORIGA, Figure 15 corresponds to 10 images from RIM-ONE DL, and Figure 16 relates to 4 images from HRF. \begin{table}
\begin{tabular}{c c c c} \hline  & **ORIGA** & **RIM-ONE DL** & **HRF** \\ \hline Epoch & 15 & 15 & 15 \\ Batch size & 32 & 8 & 4 \\ Train accuracy & 0.999 & 0.999 & 0.98 \\ Test Accuracy & 1.00 & 0.99 & 1.00 \\ AUC & 1.00 & 0.99 & 1.00 \\ Precision & 1.00 & 0.99 & 1.00 \\ Recall & 1.00 & 0.99 & 1.00 \\ F1 score & 1.00 & 0.99 & 1.00 \\ Specificity & 1.00 & 1.00 & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 31: Summary of the optimal classification results for the proposed EfficientNetB0 model on the ORIGA, HRF, and RIM-ONE DL datasets. Figure 12: The training and validation loss, as well as accuracy over 15 epochs for the RIM-ONE DL dataset.