Glaucoma Classification on retinal Glaucoma dataset (test)
99.3AccuracyDeep Learning inspired Approach
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Deep Learning inspired Approachclassifier=SVM, features=Deep learning-derived features integrated with manually extracted ellipse-based features, Study=Our Study2026.06 | 99.3 | — | |
| Optical Coherence Tomography Angiogram ImagesStudy=Oh et al. [21]2026.06 | 94.3 | — | |
| Fractal DimensionsStudy=Kolar and Jan [17]2026.06 | 93.8 | — | |
| DenseNet FeaturesStudy=Prananda et al. [29]2026.06 | 92.88 | — | |
| Image Processing based Approachpipeline=image processing only, Study=Our Study2026.06 | 92.31 | — | |
| Texture and Higher Order Spectra FeaturesStudy=Acharya et al. [19]2026.06 | 91 | — | |
| FiLM-EnsembleM=162022.05 | 0.878 | 0.055 | |
| BatchEnsembleM=162022.05 | 0.871 | 0.066 | |
| FiLM-EnsembleM=82022.05 | 0.869 | 0.068 | |
| Deep EnsembleM=162022.05 | 0.868 | 0.066 | |
| BatchEnsembleM=82022.05 | 0.868 | 0.071 | |
| FiLM-EnsembleM=42022.05 | 0.868 | 0.074 | |
| BatchEnsembleM=42022.05 | 0.865 | 0.063 | |
| FiLM-EnsembleM=22022.05 | 0.863 | 0.062 | |
| Deep EnsembleM=82022.05 | 0.86 | 0.091 | |
| FSSD2022.05 | 0.859 | 0.047 | |
| Deep EnsembleM=42022.05 | 0.857 | 0.078 | |
| Deep EnsembleM=22022.05 | 0.856 | 0.041 | |
| PNML2022.05 | 0.856 | 0.061 | |
| SNGP2022.05 | 0.847 | 0.064 | |
| BatchEnsembleM=22022.05 | 0.845 | 0.035 | |
| SingleM=12022.05 | 0.844 | 0.084 | |
| MasksembleM=42022.05 | 0.83 | 0.021 | |
| MC-DropoutM=162022.05 | 0.827 | 0.053 | |
| MasksembleM=22022.05 | 0.827 | 0.049 | |
| MasksembleM=162022.05 | 0.817 | 0.063 | |
| MasksembleM=82022.05 | 0.802 | 0.062 | |
| MC-DropoutM=82022.05 | 0.8 | 0.046 | |
| MC-DropoutM=42022.05 | 0.784 | 0.049 | |
| MIMOM=22022.05 | 0.724 | 0.061 | |
| MIMOM=42022.05 | 0.698 | 0.082 | |
| MIMOM=82022.05 | 0.689 | 0.041 | |
| MIMOM=162022.05 | 0.683 | 0.064 | |
| MC-DropoutM=22022.05 | 0.67 | 0.002 |