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

C. H. Huck Yang, Fangyu Liu, Jia-Hong Huang, Meng Tian, M. D. I-Hung Lin, Yi Chieh Liu, Hiromasa Morikawa, Hao Hsiang Yang, Jesper Tegner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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 (https://github.com/huckiyang/EyeNet2).
Original languageEnglish (US)
Title of host publicationComputer Vision – ACCV 2018 Workshops
PublisherSpringer Nature
Pages323-338
Number of pages16
ISBN (Print)9783030210731
DOIs
StatePublished - Jun 19 2019

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