Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

Abstract

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 labels collection sorted by the professional ophthalmologist from the educational project Retina Image Bank, called EyeNet, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet, our model achieves 90.40% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists.

Synthesizing New Retinal Symptom Images by Multiple Generative Models

“…In [9], the authors trained a network that classifies different retinal diseases; 52 kinds of retinal diseases are labeled and classified. Proposed methods [23] include three frameworks: U-net, SVM and ResNet50; predicton by ResNet50 performs best. Therefore, ResNet50 is modified so that the generated images also can be classified to make sure of their correctness.…”