## 5 Conclusions and Future Work  
This project showcases the potential of deep learning, specifically U-Net with the ResNet34 encoder and EfficientNetB0, for automated glaucoma detection from retinal  
Figure 19: The area under the ROC (AUROC) for the three-test dataset of our proposed EfficientNetB0 model: (**a**): ORIGA; (**b**): RIM-ONE DL; (**c**): HRF datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Performance Criteria**} \\ \hline \multirow{2}{*}{**Study**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**AUC**} & \multicolumn{2}{c}{**F-Measure**} & \multirow{2}{*}{**IOU**} & \multicolumn{2}{c}{**Computational**} \\  & & & **(Recall)** & & & & **Measure** & & **Efficiency** \\ \hline \multirow{3}{*}{[1]} & \multirow{3}{*}{ORIGA} & Train: 98.82\% & Train: & Train: & & & & & \\  & & Train: 98.95\% & 98.15\% & 98.15\% & NA & NA & NA & High processing time and memory usage. \\  & & Test: 96.90\% & Test: & Test: & 96.33\% & & & & & Execution time of 1067 s; \\  & & & & & & & & 6.2 million parameters, \\ \multirow{3}{*}{[2]} & ORIGA & \multirow{3}{*}{NA} & \multirow{3}{*}{Avg. 96.3\%} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \multirow{3}{*}{0.97\%} & \multirow{3}{*}{0.97\%} & \multirow{3}{*}{0.97\%} & \multirow{3}{*}{0.97\%} \\  & HRF & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & ORIGA & 95.75\% & 94.75\% & 94.9\% & 97.9\% & NA & NA & NA & High processing time and memory usage. \\ \multirow{3}{*}{[9]} & ORIGA & \multirow{3}{*}{97.2\%} & \multirow{3}{*}{97\%} & \multirow{3}{*}{NA} & \multirow{3}{*}{98\%} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & RIM-ONE & & & & & & & & & \\ \cline{1-1}  & DL & & & & & & & & & \\ \cline{1-1}  & & Train: 99.9\% & \multirow{3}{*}{100\%} & \multirow{3}{*}{100\%} & \multirow{3}{*}{100\%} & \multirow{3}{*}{98\%} & \multirow{3}{*}{The training times were as follows: approximately} & & & & \\ \cline{1-1}  & RIM-ONE & Train: 99.9\% & \multirow{3}{*}{99\%} & \multirow{3}{*}{100\%} & \multirow{3}{*}{99\%} & \multirow{3}{*}{99\%} & \multirow{3}{*}{98\%} & \multirow{3}{*}{240 s for ORIGA, 150 s for RIM ONE-DL, and 270 s for HRF. Our model is optimized for high} & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & HRF & Train: 98\% & \multirow{3}{*}{100\%} & \multirow{3}{*}{100\%} & \multirow{3}{*}{100\%} & \multirow{3}{*}{98\%} & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & \\ \cline{1-1} \cline{2-1}  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 32: Comparison of the proposed model with the related works. images. The models demonstrated remarkable performance across ORIGA, RIM-ONE DL, and HRF datasets, excelling in accuracy, AUC, precision, recall, F1 score, specificity, and mIoU. This study explores the impact of image preprocessing and postprocessing techniques, revealing the positive influence of augmentation techniques and a larger dataset, while denoising filters showed no enhancement. This investigation has important practical implications for the medical field. The implementation of automated glaucoma detection can enhance early diagnosis, contributing to enhanced patient experiences and reduced healthcare costs. Our model provides a high level of accuracy and reliability, indicating its potential integration into clinical workflows. This integration would support ophthalmologists and minimize the load of manual image analysis. Future work could investigate alternative preprocessing methods, testing other deep learning structures, using a more extensive and varied dataset. We also plan to apply our model in private clinics to assess its practical application in real-world settings.