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Performance comparison of the proposed approach over the existing technique
| Methods | Accuracy (%) | Precision (%) | Recall (%) | Specificity (%) | Sensitivity (%) |
|---|---|---|---|---|---|
| Deep CNN [32] | 92.5 | 92.3 | 91.3 | 90.4 | 90.3 |
| Graph CNN [33] | 92.9 | 92.9 | 91.2 | 91.3 | 90.8 |
| Ensembling [34] | 93.1 | 93.2 | 92.3 | 92.3 | 91.1 |
| U-Net+ InceptionV3 [35] | 93.6 | 94.9 | 93.7 | 93.3 | 94.2 |
| ODGNet [31] | 95.2 | 95.1 | 94.6 | 94.5 | 95.3 |
| U-Net+EfficientNet [22] | 96.5 | 95.3 | 95.7 | 95.1 | 96.3 |
| Proposed Model | 98.9 | 98.4 | 96.4 | 97.8 | 97.2 |
Comparative analysis of existing glaucoma detection approaches
| Ref | Method / Model | Dataset | Key Contribution | Limitations |
|---|---|---|---|---|
| [16] | M-LAP Model | Fundus Images | Multi-scale feature extraction for glaucoma detection | Difficulty in accurate optic cup segmentation |
| [19] | SVM-based Classification | Retinal fundus images | Uses retinal blood flow features for glaucoma classification | Requires large datasets for effective training |
| [20] | GSO Algorithm | Fundus Images | Automatic optic cup detection using intensity gradients | Performance affected by low-resolution images |
| [21] | Geometric Feature Model | Digital fundus images | Optic disc segmentation using computer vision methods | Sensitive to illumination and intensity variations |
| [23] | CNN-based CAD Model | Fundus Images | Automated glaucoma detection using deep learning | Limited training samples and incomplete attention mapping |
| [25] | AG-CNN | LAG Dataset | Attention-based CNN improves convergence and robustness | Reduced AUC and incomplete attention coverage |
| [27] | Disc-aware Ensemble Network | Fundus Images | Integrates global and local contextual information | High computational complexity |