  Automatic clinical diagnosis of retinal diseases has emerged as a promising
approach to facilitate discovery in areas with limited access to specialists.
Based on the fact that fundus structure and vascular disorders are the main
characteristics of retinal diseases, we propose a novel visual-assisted
diagnosis hybrid model mixing the support vector machine (SVM) and deep neural
networks (DNNs). Furthermore, we present a new clinical retina dataset, called
EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using
EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the model
performance is comparable to the professional ophthalmologists.
