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Retinal Oct Disease Classification On

Metrics

Acc

Results

Performance results of various models on this benchmark

Model Name
Acc
Paper TitleRepository
Joint-Attention-Network OpticNet-7199.68Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
Karri et al.96Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Lee et al.87.63Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
MobileNet-v297.46MobileNetV2: Inverted Residuals and Linear Bottlenecks-
Joint-Attention-Network ResNet50-v1100Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
MobileNet-v297.46Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Xception99.36Xception: Deep Learning With Depthwise Separable Convolutions
Lee et al.87.63Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration-
ResNet50-v194.92Deep Residual Learning for Image Recognition-
OpticNet-71100Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Joint-Attention-Network MobileNet-v299.36Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
Awais et al.93Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Xception99.36Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
ResNet50-v194.92Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
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