ArticlePloS one2022
Data augmentation using Variational Autoencoders for improvement of respiratory disease classification.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Respiratory sound analysis for ICU clinical decision support: deep learning-based classification of normal and abnormal sounds using real ICU data.BMC medical informatics and decision making · 2026Article
- Automated detection of fetal vascular malperfusion via data augmentation and algorithm improvement.Scientific reports · 2026Article
- A lightweight intrusion detection approach for CAN bus using depthwise separable convolutional Kolmogorov Arnold network.Scientific reports · 2025Article
- Weighted-VAE: A deep learning approach for multimodal data generation applied to experimental T. cruzi infection.PloS one · 2025Article
- Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups.BMC medical research methodology · 2024Article
- Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers.Sensors (Basel, Switzerland) · 2024Article
- A New Shapley-Based Feature Selection Method in a Clinical Decision Support System for the Identification of Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Article
- Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices.Tuberculosis and respiratory diseases · 2023Article
- A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.Bioengineering (Basel, Switzerland) · 2023Article
- moBRCA-net: a breast cancer subtype classification framework based on multi-omics attention neural networks.BMC bioinformatics · 2023Article
- Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.International journal of environmental research and public health · 2023Article
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6 authors.
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Abstract
Computerized auscultation of lung sounds is gaining importance today with the availability of lung sounds and its potential in overcoming the limitations of traditional diagnosis methods for respiratory diseases. The publicly available ICBHI respiratory sounds database is severely imbalanced, making it difficult for a deep learning model to generalize and provide reliable results. This work aims to synthesize respiratory sounds of various categories using variants of Variational Autoencoders like Multilayer Perceptron VAE (MLP-VAE), Convolutional VAE (CVAE) Conditional VAE and compare the influence of augmenting the imbalanced dataset on the performance of various lung sound classification models. We evaluated the quality of the synthetic respiratory sounds' quality using metrics such as Fréchet Audio Distance (FAD), Cross-Correlation and Mel Cepstral Distortion. Our results showed that MLP-VAE achieved an average FAD of 12.42 over all classes, whereas Convolutional VAE and Conditional CVAE achieved an average FAD of 11.58 and 11.64 for all classes, respectively. A significant improvement in the classification performance metrics was observed upon augmenting the imbalanced dataset for certain minority classes and marginal improvement for the other classes. Hence, our work shows that deep learning-based lung sound classification models are not only a promising solution over traditional methods but can also achieve a significant performance boost upon augmenting an imbalanced training set.
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