Investigation into the Classification of Cough Sounds for Early Asthma Screening.

Journal: Current allergy and asthma reports
Published Date:

Abstract

PURPOSE OF REVIEW: This review aims to explore an effective and scalable approach for early asthma detection using cough sounds. The main objective is to evaluate whether a multi-model deep learning fusion framework can improve diagnostic accuracy and generalizability in real-world settings.

Authors

  • Yanming Huo
    School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China. 1213101415@qq.com.
  • Jiajing Ma
    School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China.
  • Huixian Liu
    Department of Radiology, Shenzhen Baoan Women's and Children's Hospital, #56, Yulv St., Baoan District, Shenzhen, Guangdong, 518102, People's Republic of China.
  • Luyuan Jia
    School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China.
  • Guo Zhang
    CHESS-COVID-19 Group, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
  • Congkang Zhang
    School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China.
  • Xu Guo
    Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Shen-Ao Hao
    School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China.
  • Yongdong Song
    School of Electrical Engineering, Hebei University of Science and Technology, No. 26 Yuxiang Street, Yuhua District, Shijiazhuang, Hebei, China.
  • Haotian Sun