Table 1.
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 |
Figure 1.
Workflow of Proposed Work
Figure 2.
Structure of generator
Figure 3.
Structure of discriminator network
Figure 4.
Sample images from the dataset
Figure 5.
Synthetic output after GAN applied
Figure 6.
Segmented output
Table 2.
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 |
Figure 7.
Accuracy of proposed over existing
Figure 8.
Precision of proposed over existing
Figure 9.
Recall of proposed over existing
Figure 10.
Specificity of proposed over existing
Figure 11.
Sensitivity of proposed over existing