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Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features
Content Provider | MDPI |
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Author | Jung, Shing-Yun Liao, Chia-Hung Wu, Yu-Sheng Yuan, Shyan-Ming Sun, Chuen-Tsai |
Copyright Year | 2021 |
Description | 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. |
Starting Page | 732 |
e-ISSN | 20754418 |
DOI | 10.3390/diagnostics11040732 |
Journal | Diagnostics |
Issue Number | 4 |
Volume Number | 11 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-04-20 |
Access Restriction | Open |
Subject Keyword | Diagnostics Industrial Engineering Lung Sounds Convolutional Neural Network Feature Extraction Automatic Auscultations Depthwise Separable Convolution |
Content Type | Text |
Resource Type | Article |