YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
Chun-Tse Chien Rui-Yang Ju Kuang-Yi Chou Jen-Shiun Chiang
The introduction of YOLOv9, the latest version of the You Only Look Once
(YOLO) series, has led to its widespread adoption across various scenarios.
This paper is the first to apply the YOLOv9 algorithm model to the fracture
detection task as computer-assisted diagnosis (CAD) to help radiologists and
surgeons to interpret X-ray images. Specifically, this paper trained the model
on the GRAZPEDWRI-DX dataset and extended the training set using data
augmentation techniques to improve the model performance. Experimental results
demonstrate that compared to the mAP 50-95 of the current state-of-the-art
(SOTA) model, the YOLOv9 model increased the value from 42.16% to 43.73%, with
an improvement of 3.7%. The implementation code is publicly available at
https://github.com/RuiyangJu/YOLOv9-Fracture-Detection.