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YOLOv11 models achieve high accuracy in automated wound segmentation and classification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed two new instance segmentation models based on the YOLOv11 architecture for automated wound assessment. These models are designed to perform both boundary segmentation and classification across five distinct wound types, including burn injuries, pressure injuries, diabetic foot ulcers, vascular ulcers, and surgical wounds. The study utilized a balanced dataset of 2,963 annotated images, with augmentation techniques significantly improving performance, particularly for visually subtle burn injuries. YOLOv11x demonstrated superior boundary segmentation, while YOLOv11m and YOLOv11l achieved high accuracy in wound classification, with the lightweight YOLOv11n offering a balance of accuracy and computational efficiency for practical deployment. AI

IMPACT Potential to improve clinical decision-making and remote care through accurate, real-time wound analysis.

RANK_REASON Academic paper detailing new models and their performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YOLOv11 models achieve high accuracy in automated wound segmentation and classification

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Academic paper detailing new models and their performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Igor Melnychuk, Ming C. Leu ·

    Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification

    arXiv:2603.27325v2 Announce Type: replace 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…

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