{"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/automatic-classification-of-bright-retinal","title":"Automatic Classification of Bright Retinal Lesions via Deep Network Features","arxiv_id":"1707.02022","date":"2017-07-07","proceeding":null,"authors":["Ibrahim Sadek","Mohamed Elawady","Abd El Rahman Shabayek"],"abstract":"The diabetic retinopathy is timely diagonalized through color eye fundus\nimages by experienced ophthalmologists, in order to recognize potential retinal\nfeatures and identify early-blindness cases. In this paper, it is proposed to\nextract deep features from the last fully-connected layer of, four different,\npre-trained convolutional neural networks. These features are then feeded into\na non-linear classifier to discriminate three-class diabetic cases, i.e.,\nnormal, exudates, and drusen. Averaged across 1113 color retinal images\ncollected from six publicly available annotated datasets, the deep features\napproach perform better than the classical bag-of-words approach. The proposed\napproaches have an average accuracy between 91.23% and 92.00% with more than\n13% improvement over the traditional state of art methods.","url_abs":"http://arxiv.org/abs/1707.02022v3","url_pdf":"http://arxiv.org/pdf/1707.02022v3.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":"automatic-classification-of-bright-retinal","repo_url":"https://github.com/mawady/DeepRetinalClassification","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}