{"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/skin-disease-identification-from-dermoscopy","title":"Skin disease identification from dermoscopy images using deep convolutional neural network","arxiv_id":"1807.09163","date":"2018-07-24","proceeding":null,"authors":["Anabik Pal","Sounak Ray","Utpal Garain"],"abstract":"In this paper, a deep neural network based ensemble method is experimented\nfor automatic identification of skin disease from dermoscopic images. The\ndeveloped algorithm is applied on the task3 of the ISIC 2018 challenge dataset\n(Skin Lesion Analysis Towards Melanoma Detection).","url_abs":"http://arxiv.org/abs/1807.09163v1","url_pdf":"http://arxiv.org/pdf/1807.09163v1.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":"skin-disease-identification-from-dermoscopy","repo_url":"https://github.com/NokaSe08/skindisease","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}