MobileNetV2 and NASNetMobile, pretrained on ImageNet and fine-tuned on a three-class subset of RFMiD, reach 90.8% and 89.5% accuracy in classifying normal, diabetic retinopathy, and macular hole fundus images.
Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images,
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Lightweight Convolutional Neural Networks for Retinal Disease Classification
MobileNetV2 and NASNetMobile, pretrained on ImageNet and fine-tuned on a three-class subset of RFMiD, reach 90.8% and 89.5% accuracy in classifying normal, diabetic retinopathy, and macular hole fundus images.