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

arXiv: https://arxiv.org/abs/2603.27325 · HTML: https://mlsanity.com/paper/2603.27325/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series](https://mlsanity.com/paper/2406.19407/) (2024)
- [YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions](https://mlsanity.com/paper/2411.00201/) (2024)
- [Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation](https://mlsanity.com/paper/2111.01590/) (2021)
- [Image Based Artificial Intelligence in Wound Assessment: A Systematic Review](https://mlsanity.com/paper/2009.07141/) (2020)
- [Multi-modal Wound Classification using Wound Image and Location by Deep Neural Network](https://mlsanity.com/paper/2109.06969/) (2021)
- [Integrated Image and Location Analysis for Wound Classification: A Deep Learning Approach](https://mlsanity.com/paper/2308.11877/) (2023)
- [Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study](https://mlsanity.com/paper/2406.16012/) (2024)
- [A Mobile App for Wound Localization using Deep Learning](https://mlsanity.com/paper/2009.07133/) (2020)
- [YOLOv11 Optimization for Efficient Resource Utilization](https://mlsanity.com/paper/2412.14790/) (2024)
- [Development of a deep learning-based tool to assist wound classification](https://mlsanity.com/paper/2303.16522/) (2023)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards](https://mlsanity.com/paper/2412.05728/) (2024)

## Abstract (arXiv)

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 created to train the models for both tasks, using five-fold cross-validation. Models trained on the original, non-augmented dataset performed consistently across folds, though BI detection accuracy was relatively low; augmenting the dataset with rotation, flipping, and variations in brightness, saturation, and exposure significantly improved performance, particularly for visually subtle BI cases. Among the tested variants, YOLOv11x achieved the best WBS performance (F1-score: 0.9341; mAP50: 0.9629). For WC, YOLOv11m achieved the highest mAP50 (0.9194) and mAP50-95 (0.6950), whereas YOLOv11l achieved the highest F1-score (0.8797). The lightweight YOLOv11n provided comparable accuracy at lower computational cost, making it suitable for resource-constrained deployments. Supported by confusion matrices and visual detection outputs, the results confirm robustness against complex backgrounds and high intra-class variability, demonstrating the potential of YOLOv11-based architectures for accurate, real-time wound analysis in clinical and remote care settings.