Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
AuthorsHuck Yang, C. H.
I-Hung Lin, M. D.
Liu, Yi Chieh
Yang, Hao Hsiang
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Earth Science and Engineering
Earth Science and Engineering Program
Preprint Posting Date2018-11-01
Online Publication Date2019-06-19
Print Publication Date2019
Permanent link to this recordhttp://hdl.handle.net/10754/656187
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AbstractAutomatic 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).
CitationHuck Yang, C.-H., Liu, F., Huang, J.-H., Tian, M., I-Hung Lin, M. D., Liu, Y. C., … Tegnèr, J. (2019). Auto-classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model. Lecture Notes in Computer Science, 323–338. doi:10.1007/978-3-030-21074-8_28
Conference/Event name14th Asian Conference on Computer Vision, ACCV 2018
Except where otherwise noted, this item's license is described as The final publication is available at Springer via 10.1007/978-3-030-21074-8_28