DEEP LEARNING-BASED WOUND IMAGE SEGMENTATION: A COMPREHENSIVE REVIEW OF METHODS, ARCHITECTURES, AND CHALLENGES

Chronic wounds affect millions of people worldwide and impose a considerable burden on healthcare systems. Accurate wound assessment, particularly wound segmentation, is a crucial step in automated wound analysis, enabling precise wound area measurement, tissue classification, and healing monitoring. In recent years, deep learning techniques have demonstrated remarkable performance in medical image segmentation and have shown significant potential for wound image analysis. This paper presents a comprehensive review of deep learning-based methods for automatic wound image segmentation. Existing approaches are categorized into six major groups: (۱) standard convolutional neural networks (CNNs) and feed-forward networks, (۲) U-Net and its variants, (۳) Mask R-CNN-based instance segmentation methods, (۴) hybrid approaches combining deep learning with traditional feature extraction techniques, (۵) semi-supervised and active learning methods, and (۶) data augmentation techniques, including generative adversarial networks (GANs). The reviewed methods are analyzed and compared using commonly reported evaluation metrics such as Dice coefficient, Intersection over Union (IoU), precision, recall, and F۱-score. In addition, this review discusses the major challenges in wound image segmentation, including limited annotated datasets, variability in wound appearance, illumination changes, and class imbalance. Finally, potential future research directions are highlighted, including vision transformers, foundation models, multimodal imaging, and explainable artificial intelligence.

کلیدواژه ها:

Wound segmentation ، deep learning ، convolutional neural networks ، U-Net ، Mask RCNN ، medical image analysis ، chronic wounds