## 5 Results and Discussion
### Comparison and Discussion with Previous Studies  
For segmentation, we compared our proposed optic disc segmentation method with other studies on Drishti-GS, using average sensitivity, specificity, accuracy, DICE score, and Jaccard, and the results are presented in Table 16. According to these results, the proposed model achieved the highest Jaccard, recall, and specificity performance. Also, the accuracy and DICE performance of the proposed approach are quite close to other studies. These findings demonstrate the effectiveness of the proposed methodology in segmenting the OD. There are previous studies that classify glaucoma based on the data sets in our study. However, there are differences in the way the authors approached this problem. Some studies used different image-splitting rates, while others opted for cross-validation with varying folds. Based on Tables 4-15, both k-fold and hold-out validation approaches were used in this study. Table 17 shows that none of the studies used hold-out and k-fold cross-validation methods for both cropped (two-stage) and uncropped images (one-stage). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Studies** & **R** & **S** & **Acc** & **DICE** & **JACCARD** \\ \hline [PERSON] et al. [38] & 95.78 & 97.83 & 97.64 & - & - \\ \hline [PERSON] [39] & - & - & 97.3 & - & - \\ \hline [PERSON] [40] & - & - & - & 94.3 & 89.3 \\ \hline [PERSON] [41] & & & & 85 & \\ \hline [PERSON] et al. [42] & - & - & 99.69 & 94.9 & 90.4 \\ \hline [PERSON] et al. [43] & 95.28 & 99.43 & 99.31 & 88.43 & - \\ \hline Our Study & 96.04 & 99.58 & 99.39 & 94.15 & 90.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison of different methods for segmentation on the Drishti-GS dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author(s)** & **Methods** & **Acc** & **R** & **S** & **P** & **F1** & **Dataset** & **Split** & **Cropped ROI** \\ \hline [PERSON] et al. [17] & CNN with & 86.62 & 92.31 & 48.15 & 92.38 & - & DRISHTI-GS & Hold-out & Not Applied \\ \cline{2-8}  & 18 layers & 78.32 & 58.06 & 92.44 & 84.36 & - & ORIGA & \\ \hline Elangovanetal [44] & VGG-19 & 91.50 & 98.09 & 49.17 & 92.87 & - & DRISHTI-GS & Hold-out & Not Applied \\ \cline{2-8}  & ResNet-101 & 80.50 & 68.60 & 88.80 & 81.20 & - & ORIGA & \\ \hline  & ShuffleNet(P1) & 86.67 & - & - & - & - & DRISHTI-GS & \\ \cline{2-8}  & MobileNet(P1) & 81.54 & - & - & - & - & ORIGA & \\ \cline{2-8}  & Inception +SVM(P2) & 91.53 & - & - & - & - & DRISHTI-GS & \\ \cline{2-8} Sreng [15] & ResNet+SVM(P2) & 78.97 & - & - & - & - & ORIGA & Hold-out & Segmentation \\ \cline{2-8}  & Ensemble of P1 & 85.19 & - & - & - & - & DRISHTI-GS & MDCNN \\ \cline{2-8}  & Ensemble of P2 & 88.86 & - & - & - & - & ORIGA & \\ \cline{2-8}  & Ensemble of P2 & 92.06 & - & - & - & - & DRISHTI-GS & \\ \cline{2-8}  & Ensemble of P2 & 85.26 & - & - & - & - & ORIGA & \\ \hline [PERSON] et al. [45] & Xception architecture & 75.25 & 74.19 & 71.43 & - & - & DRISHTI-GS & 10-fold & Not applied \\ \hline [PERSON] et al. [46] & random forest classifier + smote & 76.9 & 79.9 & 73.8 & - & - & ORIGA & Hold-out & segmented OD regions using U-net \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparing the research classification results to the results of previous studies’ results. When comparing the results obtained on the Drishti-GS dataset with previous studies, the main achievement of this study is that it finds the Swin Transformer algorithm had a 100% classification accuracy and accurately detected normal and glaucoma images. This means that the Swin Transformer can precisely predict negative and positive results. It is worth noting that none of the reviewed studies, even those that used holdout or cross-validation, achieved such a high level of all prominence metrics. These findings highlight our approach and its potential for accurate glaucoma detection in the uncropped Drishti-GS dataset. In three-fold cross-validation, the highest classification accuracy was obtained with Swin Transformer (Acc = 88.18%).