ArticleSensors (Basel, Switzerland)2022
Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 170 citations in OpenAlex.
- Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Explainable Multi-Modal Deep Learning for Recording-Level Classification of Respiratory Audio Signals Under Internal and Domain-Shift Evaluation.Life (Basel, Switzerland) · 2026Article
- Multimodal Deep Learning Approaches for Lung Disease Detection: A Review.Medicina (Kaunas, Lithuania) · 2026Review
- Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma.BMC medical informatics and decision making · 2026Observational
- A Novel AAF-SwinT Model for Automatic Recognition of Abnormal Goat Lung Sounds.Animals : an open access journal from MDPI · 2026Article
- Artificial Intelligence for Early Detection and Prediction of Chronic Obstructive Pulmonary Disease Exacerbations.Healthcare (Basel, Switzerland) · 2026Review
- Deep Learning-Based Classification of Common Lung Sounds via Auto-Detected Respiratory Cycles.Bioengineering (Basel, Switzerland) · 2026Article
- LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.Scientific reports · 2026Article
- Article
- Multimodal data driven machine learning approach for enhancing diagnostic accuracy and efficiency in pulmonary disease management within primary healthcare settings.Frontiers in medicine · 2026Article
- Adaptive federated learning with differential privacy for multi-class respiratory disease recognition from lung sound recordings.Frontiers in digital health · 2026Article
- Artificial Intelligence Enabled Lung Sound Auscultation in the Early Diagnosis and Subtyping of Interstitial Lung Disease.Journal of clinical medicine · 2025Review
- Lightweight Hybrid Deep Learning Models for Accurate Classification of Respiratory Conditions from Raw Lung Sounds.Journal of medical systems · 2025Article
- Comparison of Deep Neural Networks for the Classification of Adventitious Lung Sounds.Journal of clinical medicine · 2025Article
- Chronic obstructive pulmonary disease screening using time-frequency features of self-recorded respiratory sounds.JAMIA open · 2025Article
- Automated Lightweight Model for Asthma Detection Using Respiratory and Cough Sound Signals.Diagnostics (Basel, Switzerland) · 2025Article
- TriSpectraKAN: a novel approach for COPD detection via lung sound analysis.Scientific reports · 2025Article
- An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.Scientific reports · 2025Article
- E-RespiNet: An LLM-ELECTRA driven triple-stream CNN with feature fusion for asthma classification.PloS one · 2025Article
- Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient's quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies-namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.
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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.