כתבה
arXiv cs.AI ·
הערכת פצעים אוטומטית
Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification
מחקר זה מציג מודלים להערכת פצעים אוטומטית באמצעות סגמנטציה. המודלים מבוססים על YOLOv11 ומאפשרים זיהוי וסיווג פצעים מדויקים.
תקציר מקורי באנגליתarXiv:2603.27325v2 Announce Type: replace-cross Abstract: Accurate wound classification (WC) and boundary segmentation are essential for guiding clinical decisions in chronic and acute wound management. However, most existing artificial intelligence (AI) models are limited, focusing on a narrow set of wound types, limited variations in wound severity, or a single task (segmentation or classification), which reduces their clinical applicability. This study presents two dedicated instance segmentation models based on You Only Look Once (YOLO)v11 that perform wound boundary segmentation (WBS) and WC across five clinically relevant wound types: burn injury (BI), pressure injury, diabetic foot ulcer, vascular ulcer, and surgical wound. A wound-type balanced dataset of 2,963 annotated images was
קרא במקור המקורי
arxiv.org
פתח כתבה מקורית