[2603.27325] Improving Automated Wound Assessment Using Joint Boundary Segmentation and Multi-Class Classification Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.27325 (cs) [Submitted on 28 Mar 2026] Title:Improving Automated Wound Assessment Using Joint Boundary Segmentation and Multi-Class Classification Models Authors:Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Igor Melnychuk, Ming C. Leu View a PDF of the paper titled Improving Automated Wound Assessment Using Joint Boundary Segmentation and Multi-Class Classification Models, by Mehedi Hasan Tusar and 3 other authors View PDF Abstract:Accurate wound classification and boundary segmentation are essential for guiding clinical decisions in both chronic and acute wound management. However, most existing AI models are limited, focusing on a narrow set of wound types or performing only a single task (segmentation or classification), which reduces their clinical applicability. This study presents a deep learning model based on YOLOv11 that simultaneously performs wound boundary segmentation (WBS) and wound classification (WC) across five clinically relevant wound types: burn injury (BI), pressure injury (PI), diabetic foot ulcer (DFU), vascular ulcer (VU), and surgical wound (SW). A wound-type balanced dataset of 2,963 annotated images was created to train the models for both tasks, with stratified five-fold cross-validation ensuring robust and unbiased evaluation. The models trained on the original non-augmented dataset achieved consistent performance across folds, though BI detection accuracy was relatively lower. Th...