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arXiv · 1806.06423

A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

C. -H. Huck Yang·Jia-Hong Huang·Fangyu Liu·Fang-Yi Chiu·Mengya Gao·Weifeng Lyu·I-Hung Lin M. D.·Jesper Tegner
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Abstract

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. We propose a novel visual-assisted diagnosis hybrid model based on the support vector machine (SVM) and deep neural networks (DNNs). The model incorporates complementary strengths of DNNs and SVM. Furthermore, we present a new clinical retina label collection for ophthalmology incorporating 32 retina diseases classes. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the model performance is comparable to the professional ophthalmologists.

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C. -H. Huck Yang, Jia-Hong Huang, Fangyu Liu, Fang-Yi Chiu, Mengya Gao, Weifeng Lyu, I-Hung Lin M. D., Jesper Tegner. 2018-06-17. A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases. https://arxiv.org/abs/1806.06423

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