## 6 Conclusions  
Optic disc detection is one of the basic steps in the automated diagnosis of glaucoma. This paper presents an automated method for the segmentation of optic discs and the classification of glaucoma. The proposed segmentation method incorporates gray scale masking [13], mathematical morphology operations (closing and dilation), and the Grey Wolf Optimization (GWO) algorithm for optimum threshold determination. We used the GWO algorithm for the first time in retinal optic disc segmentation on the DRISHTIS-GS. Four deep learning approaches were used to classify glaucoma: ResNet50, Inception V3, DenseNet201, and VGG19, as well as Swin Transform. The proposed models were tested on the demanding ORIGA and DRISHTI-GS datasets. These datasets are uneven in terms of the number of images per class. In addition, classification was carried out with a random forest model fed with features obtained from trained CNNs. All classification \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Author(s)** & **Methods** & **Acc** & **R** & **S** & **P** & **F1** & **Dataset** & **Split** & **Cropped ROI** \\ \hline \multirow{3}{*}{[PERSON] et al., 2019 [19]} & \multirow{3}{*}{CNN a with 16 layers} & 85.89 & 98.19 & 26.4 & 84.75 & - & DRISHTI GS & \multirow{3}{*}{Hold-out} & \multirow{3}{*}{Not Applied} \\  & & 71.88 & 61.62 & 79.3 & 69.44 & - & ORIGA & & \\ \cline{1-1} \cline{5-10}  & & 78.02 & 55.66 & 93.56 & 85.8 & - & DRISHTI-GS & \multirow{3}{*}{Hold-out} & \multirow{3}{*}{Not Applied} \\ \cline{5-10}  & & 86.86 & 98.7 & 67.6 & 85.21 & - & ORIGA & & \\ \hline \multirow{3}{*}{[PERSON] [48]} & linear vector & 95.05 & 95.71 & 93.55 & - & - & DRISHTI-GS & \multirow{3}{*}{Hold-out} & \multirow{3}{*}{Not Applied} \\  & quantizer-artificial & 85.38 & 83.33 & 86.10 & - & - & ORIGA & & \\ \cline{1-1} \cline{5-10}  & neural network & & 85.38 & 83.33 & 86.10 & - & - & ORIGA & \\ \hline \multirow{3}{*}{[PERSON] et al. [49]} & dynamic support & \multirow{3}{*}{85.5} & - & - & - & - & ORIGA & 10 fold & Not applied \\  & vector machine & & 99 & 97 & 100 & - & - & DRISHTI-GS & \multirow{3}{*}{5-fold} & \multirow{3}{*}{Cropped histogram} \\ \cline{1-1} \cline{5-10}  & image processing & 2D-FBSE-EWT & 92.3 & 91.9 & 94.8 & - & - & ORIGA & \\ \hline \multirow{3}{*}{Present study} & Swin Transformer & 100 & 100 & 100 & 100 & 100 & \multirow{3}{*}{DRISHTI-GS} & \multirow{3}{*}{Hold-out} & \multirow{3}{*}{Uncropped} \\ \cline{1-1} \cline{5-10}  & Swin Transformer & 88.18 & 94.29 & 74.19 & 87.19 & 85.49 & & 3-fold & \\ \cline{1-1} \cline{5-10}  & Swin Transformer & 96.15 & 95.05 & 100 & 100 & 97.46 & & \multirow{3}{*}{ORIGA} & \multirow{3}{*}{Hold-out} & \multirow{3}{*}{Cropped} \\ \cline{1-1} \cline{5-10}  & InceptionV3 & 93.84 & 97.10 & 84.52 & 92.88 & 91.77 & & 5-fold & Uncropped \\ \hline \hline \end{tabular}
\end{table} 
Table 17: _Cont._experiments were conducted on both cropped images using the YOLO algorithm and uncropped images. According to all classification results, the Swin Transformer had the highest overall performance in accurately classifying glaucoma on the ORIGA and DRISHTI-GS datasets. The comparison also reveals that both one-stage and two-stage solutions performed well on the ORIGA dataset; however, the DRISHTI-GS dataset favored the one-stage approach. Compared to existing techniques in the literature, the models achieved a higher classification accuracy rate for all the datasets. Therefore, future work should focus on identifying and recognizing other types of glaucoma abnormalities and their analysis. Investigating the effects of cropped images using other datasets, both cropped and uncropped, might also be beneficial. Methodology, H.A. and A.K. ; Software, H.A. and A.K. ; Validation, A.K. ; Investigation, A.K. ; Writing--original draft H.A. ; Writing--review & editing, A.K. ; Supervision, A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.