Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(6)

Опубликована: Окт. 9, 2024

Abstract Diabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays crucial role automated monitoring diagnosis systems, where integration of medical imaging, computer vision, statistical analysis, gait information is essential comprehensive understanding effective management. Diagnosing imperative, as it major processes diagnosis, treatment planning, neuropathy research within systems. To address this, various machine learning deep learning‐based methodologies have emerged literature to support practitioners achieving improved diagnostic analyses DFU. This survey paper investigates DFU, spanning traditional approaches cutting‐edge techniques. It systematically reviews key stages involved diabetic ulcer (DFUC) methods, including preprocessing, feature extraction, classification, explaining their benefits drawbacks. The investigation extends exploring state‐of‐the‐art convolutional neural network models tailored DFUC, involving extensive experiments with data augmentation transfer methods. overview also outlines datasets commonly employed evaluating DFUC methodologies. Recognizing that reduced blood flow lower limbs might be caused by atherosclerotic vessels, this provides recommendations researchers routine therapy prevent complications. Apart from reviewing prior literature, aims influence future diagnostics outlining prospective directions, particularly domains personalized intelligent healthcare. Finally, contribute continual evolution order provide more customized care. article categorized under: Application Areas > Health Care Technologies Machine Learning Artificial Intelligence

Язык: Английский