Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography

<div>Visual geometry group (VGG) 16 model. The overall architecture of the VGG16 model is shown. The deep convolutional neural network used ImageNet parameters; the weights of blocks 1−4 and 5 were fixed, while the fully connected layers were adjusted.</div>

Journal of Ophthalmology

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Figure 4: Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography 

Figure 4 | Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography 