YOLOv9 for fracture detection in pediatric wrist trauma X-ray images

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Title:	YOLOv9 for fracture detection in pediatric wrist trauma X-ray images
Authors:	Chien, Chun-tse;Chiang, Jen-shiun
Keywords:	biomedical imaging;computer vision;object detection;X-ray detection
Date:	2024-06-12
Issue Date:	2024-08-07 12:06:53 (UTC+8)
Publisher:	Wiley
Abstract:	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 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 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.
Relation:	ELECTRONICS LETTERS 60(11),　e13248
DOI:	10.1049/ell2.13248
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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