Research Article
Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning
Table 4
Obtained results on the Duke dataset.
| Model | Accuracy | Precision | Recall | score |
| Fine-tuning of Xception | 96.83% | 96.66 | 97% | 97% | Feature extraction from “sepconv2_bn”+CNN | 98.02% | 98.33 | 98% | 97.66% | BCNN (Xception, Xception) | 97.84% | 97% | 97% | 97% | Fine-tuning of Inception-ResnetV2 | 93.43% | 93.66% | 93.33% | 93.33% | Features extraction from “block8_1_ac”+CNN | 97.70% | 98% | 97.66% | 97.66% | BCNN (Inception-ResnetV2, Inception-Resnet-V2) | 95.55% | 95% | 95% | 95.66% | The from scratch “OCTorch-Net” | 99.68% | 99% | 96% | 97% |
|
|