Research Article
Energetic Glaucoma Segmentation and Classification Strategies Using Depth Optimized Machine Learning Strategies
Table 1
Comparison view of public dataset retina-layer coefficients. The proposed approach’s view is in bold.
| | NFL | GCL and IPL | INL | OPL | ONL | RPE |
| ME-2 (Manual-Expert) | 0.867 | 0.895 | 0.806 | 0.729 | 0.886 | 0.845 | Chiu [9] | 0.865 | 0.886 | 0.738 | 0.739 | 0.868 | 0.806 | Chakravarty [20] | 0.867 | 0.895 | 0.806 | 0.729 | 0.886 | 0.845 | Roy [21] | 0.90 | 0.94 | 0.87 | 0.84 | 0.93 | 0.90 | Bidecision | 0.861 | 0.900 | 0.781 | 0.728 | 0.941 | 0.861 | DOMLS | 0.93 | 0.95 | 0.946 | 0.962 | 0.986 | 0.953 |
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