We are pleased to announce that our paper “A Comparative Study on Distal Radius Fracture Detection: YOLOv8 and YOLOv11 versus Faster R-CNN” has been accepted for publication at MobiWIS 2025.
The study, led by Burcu Selçuk, Safa Serif, and Tacha Serif, evaluates the performance of modern YOLO architectures for automated fracture detection in clinical settings.
The research demonstrates that YOLOv11l achieves the highest precision of 96.5% and F1-score of 96.1%, setting a new benchmark for AI-assisted diagnosis in orthopedic imaging.