## 5 Results and Discussion
### Glaucoma Classification Results
#### 5.2.2 Classification Result for ORIGA Dataset  
Table 10 shows the hold-out validation classification results using both one-stage and two-stage approaches with the pre-trained CNN architectures: DenseNet201, ResNet50, Vgg19, InceptionV3, and Swin Transformer on the ORIGA dataset. In the cropped dataset, the Swin Transformer model achieved the highest accuracy among the tested models with an accuracy of 96.15%. The Swin-Transformer model also achieved the highest recall, specificity, precision, and F1 score. These results demonstrate the effectiveness of the Swin-Transformer model in glaucoma classification, regardless of whether the dataset is cropped or uncropped. The classification performances of the models with five-fold cross-validation that we obtained for the cropped dataset are shown in Table 11. The highest classification performance was obtained with the Inception V3 model with an accuracy of 93.53%. Table 12 shows the classification results achieved for the uncropped dataset using five-fold cross-validation. The results show that the Inception V3 model achieved the highest classification performance, with an accuracy of 93.84%. The five-fold cross-validation findings show that several models, such as Inception V3, DenseNet201, and the Swin Transformer, perform better on uncropped images than cropped images. Table 13 shows the outcomes of RF models trained on cropped and uncropped datasets. Based on these findings, the DenseNet201+RF model performed best on both the cropped and uncropped datasets. Table 14 presents the performance for models: DenseNet201+RF, Resnet50+RF, VGG19+RF, and Inception V3+RF five-fold validation on the cropped dataset. The results show that DenseNet201+RF, Resnet50+RF, VGG19+RF, and Inception V3+RF had a similar range of accuracy between 74% to 76%, with DenseNet201+RF demonstrating relatively high accuracy among all models. Table 15 presents the performance of the DenseNet201+RF, ResNet50+RF, VGG19+RF, and Inception V3+RF models with 5-fold validation on the uncropped dataset. All models had similar accuracy metrics ranging from 74% to 74.76%, with the highest result obtained by Inception V3+RF. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pre-Trained CNN** & **Accuracy** & **Recall** & **Specificity** & **F1\_Score** & **Precision** \\
**Model** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multicolumn{6}{c}{cropped (two-stage approach)} \\ \hline DenseNet201 & 69.33 & 72.44 & 52.17 & 80 & 89.32 \\ \hline ResNet50 & 73.85 & 96 & 73.85 & 62.74 & 54.53 \\ \hline Vgg19 & 75.38 & 75.38 & N & 84.96 & 100 \\ \hline Inception V3 & 74.62 & 76.03 & 55.56 & 84.79 & 95.83 \\ \hline Swin Transformer & 96.15 & 95.05 & 100 & 97.46 & 100 \\ \hline \multicolumn{6}{c}{uncropped (one-stage approach)} \\ \hline DenseNet201 & 78.15 & 79.17 & 74.19 & 85.20 & 92.23 \\ \hline ResNet50 & 73.85 & 96 & 73.85 & 62.74 & 54.53 \\ \hline Vgg19 & 72.31 & 96.91 & 0 & 83.93 & 74.02 \\ \hline Inception V3 & 69.23 & 100 & 0 & 81.82 & 69.23 \\ \hline Swin Transformer & 86.15 & 88.24 & 78.57 & 90.91 & 93.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of hold-out validation classification of one-stage and two-stage approaches for the ORIGA dataset.