Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
利用深度学习和手工特征融合自动检测儿童先天性心脏病的 Phonocardiogram
机构 * Electrical and Computer System Engineering, Monash University(莫纳什大学电气与计算机系统工程系) ; Biomedical Engineering, McGill University(麦吉尔大学生物医学工程系) ; Electrical and Electronic Engineering (EEE), United International University(国际大学电气与电子工程系) ; Healthcare Engineering Innovation Group (HEIG), Department of Biomedical Engineering and Biotechnology, Khalifa University(卡利法大学健康工程创新组,生物医学工程与生物技术系) ; Department of Electrical and Computer Engineering (ECE), Duke University(杜克大学电气与计算机工程系)
专题命中 诊断辅助 :diagnosis(abstract);分类 cs.CV、cs.LG
AI总结 本文提出利用深度学习和手工特征融合方法,通过 Phonocardiogram 识别儿童先天性心脏病,实验结果显示模型在准确率、灵敏度和特异度上均达到91%以上,适用于资源有限地区。
Comments 9 Pages, 5 figures. Computers in Biology and Medicine, 2025