Paper Title
A Survey on Deep Learning Approaches for Diabetic Foot Ulcer Detection and Classification

Abstract
Diabetic Foot Ulcers (DFUs) are a serious complication of diabetes mellitus. They often lead to infection, hospitalization, and lower-limb amputation. Traditional methods for assessing DFUs rely on manual grading and clinical judgment, which can be subjective. These methods also have limited scalability and can delay intervention, especially in areas with fewer resources. Recently, deep learning (DL) has become a game changer in medical image analysis. It offers better performance in detecting, classifying, segmenting, and scoring the severity of DFUs. This survey reviews the latest DL techniques used in DFU analysis across different imaging methods, such as RGB, thermal, infrared, and hyperspectral data. It evaluates various model structures like CNNs, U-Nets, ResNets, Vision Transformers, and hybrid ensembles, as well as methods for preprocessing, few-shot learning, and combining different types of data. The paper also discusses important challenges, including a shortage of datasets, shifts between different contexts, the ability to generalize findings, and the need for clinical validation. It outlines future directions such as federated learning, mobile deployment, and explainable AI. Overall, this work emphasizes the increasing potential of DL to provide scalable, understandable, and clinically integrated solutions for managing DFUs and enabling early intervention. Keywords - Diabetic Foot Ulcer, Deep Learning, CNN, U-Net, DFU Classification, Ulcer Segmentation, Explainable AI, Medical Imaging, Domain Adaptation, Telemedicine.