## 5 Results and Discussion
### Glaucoma Classification Results
#### 5.2.1 Classification Results for DRISHTI-GS Dataset

Table 4: Results of hold-out validation classification of one-stage and two-stage approaches for the DRISHTI-GS dataset. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Models** & **Fold** & **TP** & **TN** & **FP** & **FN** & **R** & **S** & **P** & **F1** & **Acc** \\ \hline \multirow{8}{*}{DenseNet-201} & Fold 1 & 21 & 8 & 2 & 3 & 87.50 & 80.00 & 85.84 & 85.49 & 85.29 \\  & Fold 2 & 21 & 5 & 6 & 2 & 91.0 & 45.45 & 75.72 & 84.00 & 76.47 \\  & Fold 3 & 23 & 4 & 6 & 0 & 100 & 40.00 & 85.58 & 78.97 & 81.82 \\ \cline{2-10}  & Overlapped & & & & & & & & \\  & Glaucoma & 65 & 17 & 14 & 5 & 92.86 & 54.84 & 82.28 & 87.25 & \\  & Normal & 17 & 65 & 5 & 14 & 54.84 & 92.86 & 77.27 & 64.15 & \\  & Average & & & & & 73.85 & 73.85 & 79.78 & 75.7 & 81.19 \\ \hline \multirow{8}{*}{RESNET50} & Overlapped & & & & & & & & & \\  & Glaucoma & 66 & 16 & 15 & 4 & 94.29 & 51.61 & 81.48 & 87.42 & \\  & Normal & 16 & 66 & 4 & 15 & 51.61 & 94.29 & 62.74 & 62.75 & \\  & Average & & & & & 72.95 & 72.11 & 75.09 & 81.19 \\ \hline \multirow{8}{*}{Inception V3} & Overlapped & & & & & & & & \\  & Glaucoma & 63 & 16 & 15 & 7 & 90.00 & 51.61 & 80.77 & 85.14 & \\  & Normal & 16 & 63 & 7 & 15 & 51.61 & 90.00 & 69.57 & 59.26 & \\  & Average & & & & & 70.81 & 70.81 & 75.17 & 72.20 & 78.28 \\ \hline \multirow{8}{*}{VGG19} & Overlapped & & & & & & & & & \\  & Glaucoma & 64 & 13 & 18 & 6 & 91.43 & 41.94 & 78.05 & 84.21 & \\  & Normal & 13 & 64 & 6 & 18 & 41.94 & 91.43 & 68.42 & 52 & \\  & Average & & & & & 66.69 & 66.69 & 73.24 & 68.11 & 76.20 \\ \hline \multirow{8}{*}{Swin Transformer} & Overlapped & & & & & & & & \\  & Glaucoma & 66 & 10 & 21 & 4 & 94.29 & 32.26 & 75.87 & 84.08 & \\ \cline{1-1}  & Normal & 10 & 66 & 4 & 21 & 32.26 & 94.29 & 71.43 & 44.44 & \\ \cline{1-1}  & Average & & & & & & 63.28 & 63.28 & 73.65 & 64.26 & 75.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Three-fold cross-validation classification result for cropped images (two-stage approach) in the DRISHTI-GS dataset. Table 7 presents the hold-out validation classification results for the RF model using cropped images and uncropped images. As shown in Table 7, the RF model fed with features extracted from the DenseNet201 model had the highest accuracy of all models, at 95.24%. It correctly classified glaucoma cases with 100% accuracy and healthy individuals with 83.33% accuracy. When compared to other models, this model provides improvement in correctly classifying glaucoma but does not show improvement in identifying healthy individuals. The highest results obtained with ResNet50+RF and VGG19+RF models had an accuracy of 90.48%, which is the same performance value previously obtained in CNN-based models. However, their results were lower compared to the performance achieved in the uncropped dataset. For most of the CNN architectures, the cropped and uncropped accuracies were relatively close. However, there were some cases where there was a noticeable difference. For example, DenseNet201+RF had a cropped accuracy of 95.24% compared to the uncropped (90.48%). However, the best result for hold-out was obtained by the Swin transformer in all experiments.