{"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/dfunet-convolutional-neural-networks-for","title":"DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification","arxiv_id":"1711.10448","date":"2017-11-28","proceeding":null,"authors":["Manu Goyal","Neil D. Reeves","Adrian K. Davison","Satyan Rajbhandari","Jennifer Spragg","Moi Hoon Yap"],"abstract":"Globally, in 2016, one out of eleven adults suffered from Diabetes Mellitus.\nDiabetic Foot Ulcers (DFU) are a major complication of this disease, which if\nnot managed properly can lead to amputation. Current clinical approaches to DFU\ntreatment rely on patient and clinician vigilance, which has significant\nlimitations such as the high cost involved in the diagnosis, treatment and\nlengthy care of the DFU. We collected an extensive dataset of foot images,\nwhich contain DFU from different patients. In this paper, we have proposed the\nuse of traditional computer vision features for detecting foot ulcers among\ndiabetic patients, which represent a cost-effective, remote and convenient\nhealthcare solution. Furthermore, we used Convolutional Neural Networks (CNNs)\nfor the first time in DFU classification. We have proposed a novel\nconvolutional neural network architecture, DFUNet, with better feature\nextraction to identify the feature differences between healthy skin and the\nDFU. Using 10-fold cross-validation, DFUNet achieved an AUC score of 0.962.\nThis outperformed both the machine learning and deep learning classifiers we\nhave tested. Here we present the development of a novel and highly sensitive\nDFUNet for objectively detecting the presence of DFUs. This novel approach has\nthe potential to deliver a paradigm shift in diabetic foot care.","url_abs":"http://arxiv.org/abs/1711.10448v2","url_pdf":"http://arxiv.org/pdf/1711.10448v2.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"diabetic-foot-ulcers-classification-datasets","name":"Diabetic Foot Ulcers Classification Datasets","full_name":"DTU"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}