YOLOv9 for fracture detection in pediatric wrist trauma X-ray images
學年 112
學期 2
出版（發表）日期 2024-06-15
作品名稱 YOLOv9 for fracture detection in pediatric wrist trauma X-ray images
作品名稱（其他語言）
著者 Chun-Tse Chien, Rui-Yang Ju, Kuang-Yi Chou, Jen-Shiun Chiang
單位
出版者
著錄名稱、卷期、頁數 ELECTRONICS LETTERS, Vol. 60, No. 11
摘要 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.
關鍵字
語言 en
ISSN 0013-5194
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 江正雄
審稿制度 否
國別 GBR
公開徵稿
出版型式 ,電子版,紙本