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

• DOI: 10.48550/arxiv.1806.06423
• Publication Date: 2018-01-01
• Authors: Yang, C. -H. Huck; Huang, Jia-Hong; Liu, Fangyu; Chiu, Fang-Yi; Gao, Mengya; Lyu, Weifeng; D., I-Hung Lin M.; Tegner, Jesper
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Information Retrieval (cs.IR); Computer Science - Information Retrieval; 3. Good health

Accepted at the Joint ICML and IJCAI Workshop on Computational Biology (ICML-IJCAI WCB) to be held in Stockholm SWEDEN, 2018. Referring to https://sites.google.com/view/wcb2018/accepted-papers?authuser=0<br/>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.<br/>