## 5 Results and Discussion
### Glaucoma Classification Results
#### 5.2.1 Classification Results for 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}{*}{Swin Transformer} & Fold 1 & 21 & 6 & 5 & 2 & 91.30 & 54.55 & 78.90 & 78.42 & 79.41 \\  & Fold 2 & 23 & 8 & 2 & 1 & 95.83 & 80 & 91.08 & 91.03 & 91.18 \\  & Fold 3 & 22 & 9 & 1 & 1 & 95.65 & 90 & 93.94 & 93.94 & 93.94 \\ \cline{2-10}  & Overlapped & & & & & & & & & \\ \cline{2-10}  & Glaucoma & 66 & 23 & 8 & 4 & 94.29 & 74.19 & 89.19 & 91.67 & \\  & Normal & 23 & 66 & 4 & 8 & 74.19 & 94.29 & 85.19 & 79.31 & \\  & Average & & & & & 84.24 & 84.24 & 87.19 & 85.49 & 88.18 \\ \hline \multirow{4}{*}{Inception V3} & Overlapped & & & & & & & & & \\ \cline{2-10}  & Glaucoma & 65 & 19 & 12 & 5 & 92.86 & 61.29 & 84.42 & 88.44 & \\  & Normal & 19 & 65 & 5 & 12 & 61.29 & 92.86 & 79.17 & 69.09 & \\  & Average & & & & & & 77.08 & 81.80 & 78.77 & 83.18 \\ \hline \multirow{4}{*}{DenseNet-201} & Overlapped & & & & & & & & & \\ \cline{2-10}  & Glaucoma & 67 & 14 & 17 & 3 & 95.71 & 45.16 & 79.76 & 87.01 & \\  & Normal & 14 & 67 & 3 & 17 & 45.16 & 95.71 & 82.35 & 58.33 & \\  & Average & & & & & 70.44 & 70.44 & 81.06 & 72.67 & 80.21 \\ \hline \multirow{4}{*}{RESNET50} & Overlapped & & & & & & & & & \\ \cline{2-10}  & Glaucoma & 62 & 18 & 13 & 8 & 88.57 & 58.06 & 82.67 & 85.52 & \\ \cline{1-1}  & Normal & 18 & 62 & 8 & 13 & 58.06 & 88.57 & 69.23 & 63.16 & \\ \cline{1-1}  & Average & & & & & & 73.32 & 75.95 & 74.34 & 79.26 \\ \hline \multirow{4}{*}{VGG19} & Overlapped & & & & & & & & & \\ \cline{1-1}  & Glaucoma & 59 & 19 & 12 & 11 & 84.29 & 61.29 & 83.10 & 83.69 & \\ \cline{1-1}  & Normal & 19 & 59 & 11 & 12 & 61.29 & 84.29 & 63.33 & 62.30 & \\ \cline{1-1}  & Average & & & & & 72.79 & 72.79 & 73.22 & 72.99 & 77.21 \\ \hline \end{tabular}
\end{table} 
Table 6: Three-fold cross-validation classification result for uncropped images (one-stage approach) in the DRISHTI-GS dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multicolumn{8}{c}{Cropped Dataset (two-stage approach)} \\ \hline DenseNet201+RF & 95.24 & 100 & 83.33 & 95.10 & 95.54 \\ \hline Resnet50+RF & 90.48 & 100 & 66.67 & 89.82 & 91.60 \\ \hline Vgg19+RF & 90.48 & 100 & 66.67 & 89.82 & 91.60 \\ \hline InceptionV3+RF & 85.71 & 93.33 & 66.67 & 85.30 & 85.36 \\ \hline \multicolumn{8}{c}{Uncropped Dataset (one-stage approach)} \\ \hline DenseNet201+RF & 90.48 & 100 & 66.67 & 89.82 & 91.60 \\ \hline Vgg19+RF & 90.48 & 100 & 66.67 & 89.82 & 91.60 \\ \hline Resnet50+RF & 85.71 & 100 & 50.00 & 83.98 & 88.10 \\ \hline InceptionV3+RF & 85.71 & 100 & 50.00 & 83.98 & 88.10 \\ \hline \end{tabular}
\end{table} 
Table 7: Results of hold-out validation classification of the one-stage and two-stage approaches for RF Models in the DRISHTI-GS dataset. The three-fold cross-validation classification results for RF models using cropped datasets are presented in Table 8. The highest classification accuracy was achieved by the RF model trained with features extracted from the ResNet50+RF and DenseNet201+RF models, reaching an accuracy level of 81.22%. These models correctly classified glaucoma cases with an accuracy level of 100% and healthy individuals with an accuracy level of 38.70%. In addition to the experimental study conducted on the cropped data, it is important to perform similar experimental studies using a one-stage approach on the raw dataset. Therefore, similar experimental studies were conducted on the same models, and the classification performances are presented in Table 9. The highest classification performance was achieved by the VGG19+RF model, with an accuracy of 87.14%. This value is the second-highest classification performance achieved following the Swin Transformer.