## 4 Results and Discussion
### Pretrained Models for Classification
Figure 13: The training and validation loss, as well as accuracy over 15 epochs for the HRF dataset. We used Gradio [29], a Python library, to examine our model on unseen images. Gradio is designed interfaces for real-time interaction. Figure 17 shows our model successfully identifying glaucoma in a fundus image. In summary, the comprehensive evaluation of image preprocessing and segmentation techniques reveals valuable insights. Augmentation methods and larger datasets significantly enhance model generalization, with the removal of random adjustments further refining segmentation quality. Despite the negligible impact of denoising filters, ResNet34 consistently outperforms other pretrained backbones in segmentation tasks. The strategic extraction of ROI reduces computational load, ensuring efficient resource utilization. Post-processing techniques contribute to improved compatibility, generalization, and feature extraction. The proposed EfficientNetB0 model showcases exemplary performance across diverse datasets, affirming its efficacy in glaucoma classification and detection. The findings of this study collectively contribute to advancing the understanding and optimization of image processing methodologies in medical imaging applications. Figure 16: The true and predicted values on 4 images of test HRF dataset. Figure 14: The true and predicted values on randomly selected 10 images from the test datasets of ORIGA. Figure 15: The true and predicted values on randomly selected 10 images from the test datasets of RIM-ONE DL. The confusion metrics in Figure 18 reveal the accuracy of the classification model, with only one false negative on the RIM-ONE DL dataset, where a glaucoma case was misclassified as normal. Overall, the EfficientNetB0 model demonstrated exceptional performance across all three datasets, validating its effectiveness in glaucoma detection through high evaluation metric scores. The ROC curves of the EfficientNetB0 model on the ORIGA, RIM-ONE DL, and HRF datasets confirm its robust glaucoma detection performance, showcasing a steep upward trajectory and exceptional discriminatory power with high sensitivity and low false positive rates, as shown in Figure 19.  
Our proposed model, combining a U-Net with ResNet34 encoder for feature extraction and EfficientNet-B0 for classification, outperforms the state-of-the-art methods on the same datasets, showcasing its superior performance in glaucoma detection. In comparison to [1], which used a pretrained DenseNet-201/U-Net and achieved 98.82% training and 96.90% testing accuracy on ORIGA, our model reached 99.9% training and 100% testing accuracy on the same dataset. For [2], using DenseNet-77/Mask-RCNN, our model achieved 100% accuracy on ORIGA and HRF datasets compared to their 96.3% average. Ref. [3], utilizing a saliency map, reached 95.75% accuracy on ORIGA, while our model achieved 100%. Lastly, Ref. [9], with EfficientNet-B0/  
Figure 17: Illustration of one result generated by our model. Figure 18: The confusion matrices on the three test datasets of our proposed EfficientNetB0 model: (**a**): ORIGA; (**b**): RIM-ONE DL; (**c**): HRF. 97.2%, 98.21%, and 97.96% on ORIGA, HRF, and RIM ONE DL datasets, while our model achieved 100%, 100%, and 99% on the respective datasets. Overall, our proposed model outperforms existing methods across various metrics, suggesting its potential for future applications in glaucoma detection. Further studies could investigate its potential in other domains. Table 32 summarizes the comparison of the proposed model with the related works.