ArticleDiagnostics (Basel, Switzerland)2021
Efficiently Classifying Lung Sounds through Depthwise Separable CNN Models with Fused STFT and MFCC Features.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Explainable Multi-Modal Deep Learning for Recording-Level Classification of Respiratory Audio Signals Under Internal and Domain-Shift Evaluation.Life (Basel, Switzerland) · 2026Article
- Multi-Stage Respiratory Sound Analysis: Confidence-Driven Wheeze and Crackle Detection.IEEE transactions on bio-medical engineering · 2026Article
- Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- Sensitivity analysis of data augmentation methods on performance of deep learning model for lung sounds classification.Scientific reports · 2025Article
- Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation.Journal of clinical medicine · 2025Article
- Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped.Physical and engineering sciences in medicine · 2024Article
- Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method.Scientific reports · 2024Article
- Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification.IEEE open journal of engineering in medicine and biology · 2024Article
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices.Tuberculosis and respiratory diseases · 2023Article
- Article
- DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries.NPJ digital medicine · 2023Article
- Coal-gangue recognition via multi-branch convolutional neural network based on MFCC in noisy environment.Scientific reports · 2023Article
- Percussion and PSO-SVM-Based Damage Detection for Refractory Materials.Micromachines · 2023Article
- An accurate deep learning model for wheezing in children using real world data.Scientific reports · 2022Article
- A Novel Air-Door Opening and Closing Identification Algorithm Using a Single Wind-Velocity Sensor.Sensors (Basel, Switzerland) · 2022Article
- A deep neural network using audio files for detection of aortic stenosis.Clinical cardiology · 2022Article
- The coming era of a new auscultation system for analyzing respiratory sounds.BMC pulmonary medicine · 2022Review
- Detection of dementia on voice recordings using deep learning: a Framingham Heart Study.Alzheimer's research & therapy · 2021Observational
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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