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Retinal Oct Disease Classification On

Métriques

Acc

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Acc	Paper Title	Repository
Joint-Attention-Network OpticNet-71	99.68	Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Karri et al.	96	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Lee et al.	87.63	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
MobileNet-v2	97.46	MobileNetV2: Inverted Residuals and Linear Bottlenecks
Joint-Attention-Network ResNet50-v1	100	Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
MobileNet-v2	97.46	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Xception	99.36	Xception: Deep Learning With Depthwise Separable Convolutions
Lee et al.	87.63	Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration	-
ResNet50-v1	94.92	Deep Residual Learning for Image Recognition
OpticNet-71	100	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Joint-Attention-Network MobileNet-v2	99.36	Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
Awais et al.	93	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Xception	99.36	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
ResNet50-v1	94.92	Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

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