DETR and YOLOv5: Exploring Performance and Self-Training for Diabetic Foot Ulcer Detection

Diabetic feet are a long-term effect of diabetes mellitus that are at risk of ulceration due to neuropathy and ischemia. Early ulcer stages show subtle changes hard to recognize by the human eye, especially on darker skin types. Acquired ulcers may become chronic for various reasons, requiring extensive documentation to monitor healing progression. For early stage detection and documentation support, object detection algorithms are a key technology for prevention and care improvement. However, attendant symptoms like malformed toenails, hyperkeratosis, and rhagades display challenges regarding faulty detections. The research at hand explores two disparate state-of-the-art detection frameworks: Detection Transformer (DETR) as representative of the novel transformer-based architectures for computer vision, and You Only Look Once v5 (YOLOv5) as an expedited PyTorch port of YOLOv4 with explicit mobile-focus. Both are compared on a recently released dataset for diabetic foot ulcer detection with images typical for common wound care documentation. In addition, effects of self-training for performance improvement are investigated. Achieved results outperform those of other state-of-the-art methods. These are discussed highlighting differences and potential for further optimization.