{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-novel-hybrid-machine-learning-model-for","title":"A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases","arxiv_id":"1806.06423","date":"2018-06-17","proceeding":null,"authors":["C. -H. Huck Yang","Jia-Hong Huang","Fangyu Liu","Fang-Yi Chiu","Mengya Gao","Weifeng Lyu","I-Hung Lin M. D.","Jesper Tegner"],"abstract":"Automatic clinical diagnosis of retinal diseases has emerged as a promising\napproach to facilitate discovery in areas with limited access to specialists.\nWe propose a novel visual-assisted diagnosis hybrid model based on the support\nvector machine (SVM) and deep neural networks (DNNs). The model incorporates\ncomplementary strengths of DNNs and SVM. Furthermore, we present a new clinical\nretina label collection for ophthalmology incorporating 32 retina diseases\nclasses. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the\nmodel performance is comparable to the professional ophthalmologists.","url_abs":"http://arxiv.org/abs/1806.06423v1","url_pdf":"http://arxiv.org/pdf/1806.06423v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-novel-hybrid-machine-learning-model-for","repo_url":"https://github.com/huckiyang/EyeNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hybrid-machine-learning","task_name":"Hybrid Machine Learning"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}