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

Autor:	Yang, C. -H. Huck, Huang, Jia-Hong, Liu, Fangyu, Chiu, Fang-Yi, Gao, Mengya, Lyu, Weifeng, D., I-Hung Lin M., Tegner, Jesper
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Zdroj:	ICML-IJCAI Workshop 2018
Druh dokumentu:	Working Paper
Popis:	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. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1806.06423 Zobrazit plný text záznamu View this record from Arxiv