ArticleSensors (Basel, Switzerland)2020
Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds.Journal of medical systems · 2025Article
- Neural network based AI model for lung health assessment.Scientific reports · 2025Article
- A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.Bioengineering (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
- Machine Learning for Automated Classification of Abnormal Lung Sounds Obtained from Public Databases: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Deep learning-based lung sound analysis for intelligent stethoscope.Military Medical Research · 2023Review
- A Low-Cost AI-Empowered Stethoscope and a Lightweight Model for Detecting Cardiac and Respiratory Diseases from Lung and Heart Auscultation Sounds.Sensors (Basel, Switzerland) · 2023Article
- Article
- An accurate deep learning model for wheezing in children using real world data.Scientific reports · 2022Article
- A lightweight hybrid deep learning system for cardiac valvular disease classification.Scientific reports · 2022Article
- Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care.Journal of personalized medicine · 2022Review
- A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs.Sensors (Basel, Switzerland) · 2022Article
- Explainable machine learning for precise fatigue crack tip detection.Scientific reports · 2022Article
- The coming era of a new auscultation system for analyzing respiratory sounds.BMC pulmonary medicine · 2022Review
- Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.Journal of ambient intelligence and humanized computing · 2022Article
- Data augmentation using Variational Autoencoders for improvement of respiratory disease classification.PloS one · 2022Article
- Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds.Soft computing · 2022Article
- Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.IEEE journal of translational engineering in health and medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.