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Retinal Oct Disease Classification On Oct2017

Métriques

Acc
Sensitivity

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Acc
Sensitivity
Paper TitleRepository
InceptionV3 (limited)93.496.6Rethinking the Inception Architecture for Computer Vision-
InceptionV396.697.8Rethinking the Inception Architecture for Computer Vision-
MobileNet-v299.499.4Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Xception99.799.7Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
WideResNet-50-2 (EMA-decay=0.999)99.69-Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels-
MobileNet-v298.599.4MobileNetV2: Inverted Residuals and Linear Bottlenecks-
Joint-Attention-Network OpticNet-7177.4-Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
ResNet50-v199.399.3Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Joint-Attention-Network MobileNet-v295.6-Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
OpticNet-7199.899.8Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
ResNet50-v199.399.3Deep Residual Learning for Image Recognition-
InceptionV3 (limited)93.496.6Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Xception-99.7Xception: Deep Learning With Depthwise Separable Convolutions
InceptionV396.697.8Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images-
Joint-Attention-Network ResNet50-v192.4-Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images-
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