## 4 Results and Discussion
### U-Net with Pretrained Backbones for Segmentation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Experiment 2.1**} \\ \hline
**Dataset** & **Batch Size** & **Model** & **Augmentation Technique** & **Number of Images** & **mIoU** \\ \hline \multirow{4}{*}{ORIGA and REFUGE-TS} & \multirow{4}{*}{8} & RR, & \multirow{4}{*}{1360} & 0.94828725 \\  & & U-Net with & RHF & & 0.9441021 \\ \cline{1-1}  & & ResNet34 & & 0.8463246 \\ \cline{1-1}  & & ResNet34 & & 0.6734596 \\ \cline{1-1}  & & ResNet34 & & 0.51045746 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The mIoU for different numbers of images and augmentation techniques in Experiment 2.1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Experiment 3.1**} \\ \hline
**Dataset** & **Epoch** & **Batch Size** & **Model** & **Backbone** & **mIoU** \\ \hline \multirow{4}{*}{ORIGA and REFUGE-TS} & \multirow{4}{*}{30} & 8 & \multirow{4}{*}{U-Net with RB, CA, RHF, RH  
Experiment 2.4 achieved its highest mIoU (0.9826489) at 60 epochs, but a comparable mIoU (0.98035127) was attained at 50 epochs. Choosing the latter for improved HRF dataset results, Figure 7 and Table 16 illustrate training details. The IOU score measures the overlap between predicted and actual segmentation masks, with a higher score indicating better performance. The loss function quantifies the difference between predicted and actual masks, with lower loss signifying a better fit. The curve monitors the metrics and loss changes over epochs. The model in Figure 6 demonstrates an impressive IOU score of approximately 0.9 on both the training and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Experiment 2.4**} \\ \hline
**Dataset** & **Epoch** & **Batch Size** & **Model** & **Backbone** & **mIoU** \\ \hline ORIGA & 30 & & & & 0.976188 \\ and & 40 & & & 0.9792807 \\ RIM-ONE & 50 & 16 & U-Net & ResNet34 & 0.98035127 \\ DL & 60 & & & & 0.9826489 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The mIoU values for the different numbers of epochs in Experiment 2.4. Figure 6: The training and validation IOU and loss of model trained with 40 epochs in Experiment 1.4. Figure 7: The training and validation IOU and loss of model trained with 50 epochs in Experiment 2.4.  
validation sets, indicating precise image segmentation. Additionally, the model exhibits a low loss value of around 0.1 on both sets, suggesting effective error minimization between predictions and labels. The smooth and rapidly converging curve signifies efficient learning without overfitting or underfitting. In Figure 7, the model achieves a high IOU score (>0.98) on both training and validation sets, indicating accurate segmentation and good generalization. The model consistently improves over epochs without overfitting or underfitting. The significant reduction in loss function on both sets suggests effective error minimization and convergence to an optimal solution. Denoising filters showed no performance improvement, but augmentation techniques, larger datasets, and fewer epochs had a notably positive impact on mIoU values in our experiments. Table 17 presents a comparison between Experiment 1 and 2. Upon comparing the findings, we determined the model from sub-experiments (1.2, 2.2, 3.2, and 4.2) with the highest mIoU (0.98035127) for segmentation model. It demonstrated effective segmentation on the HRF dataset, accurately identifying ROI like OC and OD. Figure 8 shows an example of the segmentation results on the HRF dataset using the model trained for 50 epochs. Following the segmentation process, we executed a bitwise AND operation between the output mask and the original image to isolate the ROI. Cropping was employed to extract a specific ROI from the image. The results of the segmentation and the extracted ROI on the HRF dataset are depicted in Figure 9.  
Following the postprocessing step, significant transformations were observed after applying augmentation techniques to the segmented images.