Research Article
Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning
| Reference | Pathology | Dataset | Preprocessing | Used CNN | Classifier | Result Val_accuracy |
| Bhowmik et al. [12] | DME, drusen, CNV, and normal | Kaggle dataset: retinal OCT images | Resizing | InceptionV3 and VGG16 | Softmax | VGG16: 91.6% InceptionV3: 92% |
| Yang et al. [13] | Normal, dry and wet AMD | Local datasets | ā | CNN (EG) | Softmax | 96.05% |
| Wang D. and Wang L. [14] | AMD, DME, and normal | Dataset 1: SERI Dataset 2: Duke | Filtering: FastNIMeans+bilateralFilter | VGG16 VGG19 InceptionV3 CliqueNet DPN DenseNet ResNet ResNext | | Dataset 1: VGG16: 91.6% VGG19: 98.2% InceptionV3: 92.7% CliqueNet: 99% DPN: 99.6% DenseNet: 98.7% ResNet: 98.7% ResNext: 97.3% Dataset 2: VGG16: 86.3% VGG19: 95.1% InceptionV3: 85.3% CliqueNet: 98.6% DPN: 95.8% DenseNet: 94.2% ResNet: 95.2% ResNext: 94.8% |
| Vaghefi et al. [15] | Normal_Young, Normal_Old, and dry AMD | 75 participants | Resizing | Inception-ResnetV2 | Softmax | 99.8% |
| Karri et al. [16] | MD, DME, and normal | Duke | Denoising using BM3D+(flattening) | GoogLeNet | SVM | 96% |
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