Comparing YOLO and U-net deep learning algorithms in chronic wound image segmentation

Abstract Chronic wound analysis is crucial for effective wound care, necessitating precise and efficient segmentation techniques. This study explores the use of YOLOv8 and YOLO11, renowned deep learning algorithms for object detection, in medical image segmentation. A comprehensive evaluation was conducted comparing different versions of YOLOv8 and YOLO11 with the benchmark U-Net model using the FUSeg and Wound Data databases. Both databases were used simultaneously for training and testing to achieve a cross-dataset validation, ensuring the robustness of our models, and highlighting the importance of having data from different sources to represent the problem in various clinical contexts, thereby allowing AI models to be more adaptable and generalizable. Results show that both YOLO models significantly outperform U-Net in terms of segmentation accuracy, generalization, and inference speed. YOLOv8n achieved the highest performance with an IoU of 71.7%, a precision of 83.8%, a recall of 77.9%, and a DSC of 79.3%, and, while YOLO11s, the best performing of the YOLO11 models (IoU of 70.1%, Precision of 81.9%, Recall of 79.9%, and DSC of 78.5%), distinguished itself for its stability between thresholds and robustness across datasets. This research confirms that modern YOLO architectures offer fast, accurate, and robust solutions for automated wound segmentation, laying the groundwork for further development in AI-driven wound analysis and diagnosis.