{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:12:44Z","timestamp":1776100364238,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T00:00:00Z","timestamp":1753488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations.<\/jats:p>","DOI":"10.3390\/app15158321","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T10:51:14Z","timestamp":1753699874000},"page":"8321","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3416-2257","authenticated-orcid":false,"given":"Sara Seabra","family":"Reis","sequence":"first","affiliation":[{"name":"ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CIETI, ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5673-7306","authenticated-orcid":false,"given":"Luis","family":"Pinto-Coelho","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"CIETI, ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"},{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering Technology and Science, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4090-3921","authenticated-orcid":false,"given":"Maria Carolina","family":"Sousa","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"given":"Mariana","family":"Neto","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"given":"Marta","family":"Silva","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"}]},{"given":"Miguela","family":"Sequeira","sequence":"additional","affiliation":[{"name":"ISEP, Polytechnic of Porto, 4249-015 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10062","DOI":"10.34117\/bjdv8n2-107","article-title":"Diabetes Mellitus: Causas, Tratamento e Preven\u00e7\u00e3o\/Diabetes Mellitus: Causes, Treatment and Prevention","volume":"8","author":"Casarin","year":"2022","journal-title":"Braz. 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