Research Article
Optic Disc and Optic Cup Segmentation for Glaucoma Detection from Blur Retinal Images Using Improved Mask-RCNN
Table 4
Comparative analysis of the proposed approach with base models.
| Parameters | Inception-V4 | VGG-16 | ResNet-101 | ResNet-152 | DenseNet-121 | DenseNet-77 |
| Total parameters (million) | 41.2 | 119.6 | 42.5 | 58.5 | 7.1 | 6.2 | Training loss | 0.0102 | 0.5069 | 4.1611e−04 | 2.4844e−04 | 5.6427e−04 | 6.442e−04 | Test loss | 0.0686 | 0.6055 | 0.02082 | 0.0246 | 0.0159 | 0.0085 | Training accuracy | 99.74% | 83.86% | 99.99% | 100% | 100% | 100% | Test accuracy | 98.08% | 81.83% | 99.66% | 99.59% | 99.75% | 99.983% | Processing time (s) | 4042 | 1051 | 2766 | 4366 | 2165 | 1067 |
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