| | START |
| | INPUT: NS, annotation (orientation) |
| OUTPUT: Localized RoI, CMskDenseNet-77 |
| NS : Total image samples containing. |
| annotation (orientation): Mask coordinates of the glaucoma regions in the retinal image |
| Localized RoI : Region placement |
| CMskDenseNet-77- : Custom Mask-RCNN network with DenseNe-77 key points |
| SampleResolution ← [x y] |
| // Computing Mask |
| µ← AnchorsComputation (NS, annotation) |
| // Customized MaskRCNN model |
| CMskDenseNet-77← DesignCustomDenseNet-77MaskRCNN (SampleResolution, µ) |
| [ Sr, St] ← database division into train and test section |
| | // Glaucoma Region recognition from Training part |
| For each sample f in ⟶Sr |
| Compute DenseNet-77 keypoints ⟶ns |
| End For |
| Training CMskDenseNet-77over ns, and compute training time t_dense |
| ∂_dense ← PreRegionLoc(ns) |
| Ap_dense ← Evaluate_AP (DenseNet-77, ∂_dense) |
| For each sample F in ⟶ St |
| | (a) compute features by employing trained model ¥⟶βI |
| (b) [Mask, objectness_score, classLabel] ←Predict (βI) |
| (c) Output sample along with Mask, class |
| (d) ∂← [∂ Mask] |
| End For |
| Ap_¥← Evaluate framework ¥ using ∂ |
| FINISH. |
