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

中文翻译：

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鉴定糖尿病足溃疡的当前发展和方法概述：一项调查


糖尿病足溃疡 （DFU） 在不同年龄组中存在重大健康风险，给医疗保健专业人员在准确分类和分级方面带来了挑战。DFU 在自动健康监测和诊断系统中发挥着至关重要的作用，其中医学成像、计算机视觉、统计分析和步态信息的集成对于全面理解和有效管理至关重要。诊断 DFU 势在必行，因为它在自动健康监测和诊断系统中的诊断、治疗计划和神经病变研究过程中发挥着重要作用。为了解决这个问题，文献中出现了各种基于机器学习和深度学习的方法，以支持医疗保健从业者实现改进的 DFU 诊断分析。本调查论文研究了 DFU 的各种诊断方法，从传统的统计方法到尖端的深度学习技术。它系统地回顾了糖尿病足溃疡分类 （DFUC） 方法中涉及的关键阶段，包括预处理、特征提取和分类，解释了它们的优缺点。调查扩展到探索为 DFUC 量身定制的最先进的卷积神经网络模型，包括数据增强和迁移学习方法的广泛实验。该概述还概述了通常用于评估 DFUC 方法的数据集。认识到神经病变和下肢血流减少可能是由动脉粥样硬化血管引起的，本文为参与常规药物治疗的研究人员和从业者提供了预防严重并发症的建议。 除了回顾以前的文献外，本调查旨在通过概述前瞻性研究方向来影响 DFU 诊断的未来，特别是在个性化和智能医疗保健领域。最后，本概述有助于 DFU 诊断的不断发展，以提供更有效和定制的医疗服务。本文分为： 应用领域 > 医疗保健技术 > 机器学习技术 > 人工智能