{Jian Zhao and Guoxing Wang Yongfu Li Yuhang Zhang Qing Yu Xinzi Xu Yi Ma}

Abstract

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 mprove 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.