## 5 Results and Discussion
### Glaucoma Classification Results  
#### 5.2.1 Classification Results for DRISHTI-GS Dataset  
Table 4 shows the results of hold-out validation classification using both one-stage and two-stage approaches with pre-trained CNN architectures: DenseNet201, ResNet50, Vgg19, InceptionV3, and Swin Transformer on the DRISHTI-GS dataset. DenseNet201 and Vgg19 exhibit similar results. Both models had an accuracy of 90.48%, a sensitivity of 93.33%, and a specificity of 83.33%. These models correctly classified glaucoma cases with an accuracy of 90.48% and healthy cases with an accuracy of 83.33%. The ResNet50 model has a similar accuracy level to these models. However, comparing models based on accuracy may not be appropriate. While ResNet50 correctly classified glaucoma cases with 100% accuracy, they only correctly classified healthy cases with 66.67% accuracy. A similar situation exists with the InceptionV3 model. Swin Transformer achieved exceptional performance specifically in classifying glaucoma with 100% accuracy. In the case of uncropped images, Swin Transformer excelled across all metrics, achieving 100% accuracy. Using hold-out validation alone is not sufficient for testing model performance. Therefore, we used the k-fold cross-validation method to ensure better data utilization. In this study, the three-fold cross-validation method was implemented, and the classification performances obtained for the YOLO-cropped dataset (two-stage approach) are presented in Table 5. The highest classification performance was achieved with the DenseNet201 and ResNet50 models, with an accuracy of 81.19%. This accuracy value was lower compared to the one obtained from the hold-out validation method. According to the R and S metric values obtained from the overlapped confusion matrix, DenseNet201 correctly classifies glaucoma cases with an accuracy of 92.86% and healthy individuals with an accuracy of 54.84%. For ResNet50, these rates were 94.29% and 51.61%, respectively. These two models exhibited high accuracy in classifying glaucoma cases but low accuracy in classifying healthy individuals. Figure 7: Optic disc segmentation method performances using the GWO metaheuristic algorithm (**a**): retinal images; (**b**): segmentation result, (**c**): ground truth. Furthermore, the uncropped dataset was used to test the performance of these models. Table 6 presents the results with an accuracy of 88.18%; the Swin Transformer had the best classification performance. This result is compatible with the result obtained in the hold-out method. Swin Transformer correctly classified glaucoma cases with an accuracy of 94.29% and healthy individuals with an accuracy of 74.19%. Overall, the results of glaucoma on the Drishti-GS dataset demonstrate the effectiveness of pre-trained CNN models, particularly the Swin transformer, in glaucoma classification using the hold-out and three-fold methods. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1\_Score** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{Cropped Dataset (two-stage approach)} \\ \hline DenseNet201 & 90.48 & 93.33 & 83.33 & 90.48 & 90.48 \\ \hline Vgg19 & 90.48 & 93.33 & 83.33 & 90.48 & 90.48 \\ \hline ResNet50 & 90.48 & 100 & 66.67 & 89.82 & 91.60 \\ \hline Swin Transformer & 76.19 & 100 & 16.67 & 69.39 & 82.14 \\ \hline Inception V3 & 80.95 & 93.33 & 50.00 & 79.64 & 80.25 \\ \hline \multicolumn{6}{c}{Uncropped Dataset (one-stage approach)} \\ \hline Swin Transformer & 100 & 100 & 100 & 100 & 100 \\ \hline ResNet50 & 85 & 80 & 86.7 & 81.87 & 83.33 \\ \hline Inception V3 & 80 & 75 & 81.2 & 74.62 & 78.125 \\ \hline DenseNet201 & 75 & 57 & 84.6 & 70.88 & 72.62 \\ \hline Vgg19 & 75 & 66.7 & 76.5 & 71.56 & 63.09 \\ \hline \end{tabular}
\end{table}