Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone
學年 111
學期 1
出版(發表)日期 2023-01-20
作品名稱 Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone
著者 Kuo H-C; Chen S-H; Chen Y-H; Lin Y-C; C. Y. Chang; Wu Y-C; Wang T-D; Chang L-S; Tai I-H; Hsieh K-S
著錄名稱、卷期、頁數 Frontiers in Cardiovascular Medicine 22(9), p. 1-8
摘要 Introduction: Kawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD detection from cardiac ultrasound images. Methods: Specifically, object detection for the identification of coronary artery dilatation and brightness of left and right coronary artery is proposed and different AI algorithms were compared. In infants and young children, a dilated coronary artery is only 1-2 mm in diameter than a normal one, and its ultrasound images demonstrate a large amount of noise background-this can be a considerable challenge for image recognition. This study proposes a framework, named Scaled-YOLOv4-HarDNet, integrating the recent Scaled-YOLOv4 but with the CSPDarkNet backbone replaced by the CSPHarDNet framework. Results: The experimental result demonstrated that the mean average precision (mAP) of Scaled-YOLOv4-HarDNet was 72.63%, higher than that of Scaled YOLOv4 and YOLOv5 (70.05% and 69.79% respectively). In addition, it could detect small objects significantly better than Scaled-YOLOv4 and YOLOv5. Conclusions: Scaled-YOLOv4-HarDNet may aid physicians in detecting KD and determining the treatment approach. Because relatively few artificial intelligence solutions about images for KD detection have been reported thus far, this paper is expected to make a substantial academic and clinical contribution.
關鍵字 HarDNet;Kawasaki disease;Scaled-YOLOv4;coronary dilatation and brightness;deep learning;echocardiography;object detection
語言 en
ISSN 2297-055X
期刊性質 國外
收錄於 SCI
國別 CHE
出版型式 ,電子版

機構典藏連結 ( )

SDGS 尊嚴就業與經濟發展,產業創新與基礎設施