LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm

Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can b...

Full description

Saved in:
Bibliographic Details
Published in2019 IEEE Biomedical Circuits and Systems Conference (BioCAS) pp. 1 - 4
Main Authors Ma, Yi, Xu, Xinzi, Yu, Qing, Zhang, Yuhang, Li, Yongfu, Zhao, Jian, Wang, Guoxing
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2019
Subjects
acoustic signal processing
crackles
deep learning
Feature extraction
Hidden Markov models
Lung
Machine learning
respiratory sounds classification
Sensitivity
Stethoscope
Wavelet analysis
wheezes
Online AccessGet full text

Cover

More Information
Summary:Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can be used to provide an immediate diagnostic for respiratory-related diseases such as chronic obstructive pulmonary. In this work, we have developed an improved bi-ResNet deep learning architecture, LungBRN, which uses STFT and wavelet feature extraction techniques to improve the accuracy compared to the state-of-the-art works. To ensure a fair evaluation, we have adopted the official benchmark standards and the "train-and-test" dataset splitting method stated in the ICBHI 2017 challenge. As a result, we are able to achieve a performance of 50.16%, which is the best result in terms of accuracy compared to all participating teams from ICBHI 2017.
DOI:10.1109/BIOCAS.2019.8919021