{"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/auto-classification-of-retinal-diseases-in","title":"Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model","arxiv_id":"1808.05754","date":"2018-08-16","proceeding":null,"authors":["C. -H. Huck Yang","Fangyu Liu","Jia-Hong Huang","Meng Tian","Hiromasa Morikawa","I-Hung Lin","Yi-Chieh Liu","Hao-Hsiang Yang","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.\nBased on the fact that fundus structure and vascular disorders are the main\ncharacteristics of retinal diseases, we propose a novel visual-assisted\ndiagnosis hybrid model mixing the support vector machine (SVM) and deep neural\nnetworks (DNNs). Furthermore, we present a new clinical retina dataset, called\nEyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using\nEyeNet2, our model achieves 90.43\\% diagnosis accuracy, and the model\nperformance is comparable to the professional ophthalmologists.","url_abs":"http://arxiv.org/abs/1808.05754v4","url_pdf":"http://arxiv.org/pdf/1808.05754v4.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":"auto-classification-of-retinal-diseases-in","repo_url":"https://github.com/huckiyang/EyeNet2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}