## 4 Experiments
### OD Localization Using GWO  
The GWO algorithm was used to estimate the threshold value for the gray-level mask, which isolates the OD from the background, the initial stage in the detection of glaucoma. The GWO approach for optic disc detection is shown in Figure 3.  
For each image in the dataset, the preprocessing steps were applied. These steps include reading the original image as in Figure 4a, and then converting the image to grayscale by extracting the red channel as in Figure 4b, to enhance the visibility of the optic disc region and distinguish it from the background. A morphological closing operation, as seen in Figure 4c, was applied to smoothen the boundaries in the optic disc and fill in any gaps or holes. Additionally, a gray mask ([PERSON] et al. [11]) that helps to highlight relevant features was applied and unnecessary details were removed to effectively emphasize the optic disc region while suppressing the background as shown in Figure 4d. Following the grayscale mask, the next step is to apply histogram equalization. Histogram equalization is a process that redistributes the intensities of the image to improve the overall contrast. By stretching the intensity values across the entire range, histogram equalization enhances the visibility of the optic disc region and makes subsequent analysis by GWO more effective. Next, the GWO algorithm was deployed to optimize the segmentation threshold for the optic disc region. The wolves (search agents) iteratively update their positions based on the intensity values derived from the histogram analysis of the retina image. We used 30 search agents (the number of wolves). After obtaining the optimized segmentation threshold (prey), post-processing techniques were used to refine the segmented optic disc  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & \begin{tabular}{c} **Optimization** \\ **Algorithm** \\ \end{tabular} & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Epoch** \\ **Function** \\ \end{tabular} & \
\begin{tabular}{c} **Activation** \\ **Function** \\ \end{tabular} &
\begin{tabular}{c} **Base Model** \\ **Trainable** \\ \end{tabular} & **Loss Function** \\ \hline SwinTransformer & Rmsprop & 0.001 & 32 & 150 & & & \\ DenseNet201 & Rmsprop & 0.001 & 32 & 150 & & \\ Resnet50 & Adam & 0.001 & 32 & 150 & Relu & \multirow{2}{*}{False} & Categorical \\ InceptionV3 & Sgd & 0.01 & 32 & 150 & Softmax & & Cross-entropy \\ VGG19 & Adamax & 0.001 & 32 & 150 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of pre-trained CNN and ST models.  
region. This involves morphological operations to further improve the segmentation result by removing noise and enhancing the shape of the optic disc. Finally, the segmentation result shown in Figure 5a is compared with the ground truth shown in Figure 5b.  
Figure 3: Flowchart for optic disc detection.  
To evaluate the segmentation, we used various evaluation metrics such as sensitivity, specificity, accuracy, the Jaccard index, and the Dice coefficient. These metrics provide quantitative measures of the performance of the segmentation algorithm compared to the ground truth data.