Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features

Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features

Lung sounds remain vital in clinical diagnosis as they reveal associations with pulmonary pathologies. With COVID-19 spreading across the world, it has become more pressing for medical professionals to better leverage artificial intelligence for faster and more accurate lung auscultation. This resea...

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Bibliographic Details
Main Authors: Shing-Yun Jung, Chia-Hung Liao, Yu-Sheng Wu, Shyan-Ming Yuan, Chuen-Tsai Sun
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Diagnostics
Subjects:
lung sounds
convolutional neural network
feature extraction
automatic auscultations
depthwise separable convolution
Online Access:https://www.mdpi.com/2075-4418/11/4/732

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