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
Published in:Diagnostics (Basel) 2021-04, Vol.11 (4), p.732
Main Authors: Jung, Shing-Yun, Liao, Chia-Hung, Wu, Yu-Sheng, Yuan, Shyan-Ming, Sun, Chuen-Tsai
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Auscultation
automatic auscultations
Chronic obstructive pulmonary disease
convolutional neural network
Coronaviruses
COVID-19
Datasets
Deep learning
depthwise separable convolution
Engineering
feature extraction
Lung diseases
lung sounds
Medical equipment
Neural networks
Performance evaluation
Sound
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Summary: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 research aims to propose a feature engineering process that extracts the dedicated features for the depthwise separable convolution neural network (DS-CNN) to classify lung sounds accurately and efficiently. We extracted a total of three features for the shrunk DS-CNN model: the short-time Fourier-transformed (STFT) feature, the Mel-frequency cepstrum coefficient (MFCC) feature, and the fused features of these two. We observed that while DS-CNN models trained on either the STFT or the MFCC feature achieved an accuracy of 82.27% and 73.02%, respectively, fusing both features led to a higher accuracy of 85.74%. In addition, our method achieved 16 times higher inference speed on an edge device and only 0.45% less accuracy than RespireNet. This finding indicates that the fusion of the STFT and MFCC features and DS-CNN would be a model design for lightweight edge devices to achieve accurate AI-aided detection of lung diseases.
ISSN:2075-4418
2075-4418
DOI:10.3390/DIAGNOSTICS11040732