{"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-lesions-classification-using","title":"Skin Lesions Classification Using Convolutional Neural Networks in Clinical Images","arxiv_id":"1812.02316","date":"2018-12-06","proceeding":null,"authors":["Danilo Barros Mendes","Nilton Correia da Silva"],"abstract":"Skin lesions are conditions that appear on a patient due to many different\nreasons. One of these can be because of an abnormal growth in skin tissue,\ndefined as cancer. This disease plagues more than 14.1 million patients and had\nbeen the cause of more than 8.2 million deaths, worldwide. Therefore, the\nconstruction of a classification model for 12 lesions, including Malignant\nMelanoma and Basal Cell Carcinoma, is proposed. Furthermore, in this work, it\nis used a ResNet-152 architecture, which was trained over 3,797 images, later\naugmented by a factor of 29 times, using positional, scale, and lighting\ntransformations. Finally, the network was tested with 956 images and achieve an\narea under the curve (AUC) of 0.96 for Melanoma and 0.91 for Basal Cell\nCarcinoma.","url_abs":"http://arxiv.org/abs/1812.02316v1","url_pdf":"http://arxiv.org/pdf/1812.02316v1.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-lesions-classification-using","repo_url":"https://github.com/aryanmisra/Skin-Lesion-Classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","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}