ArticlePeerJ. Computer science2021
Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 3 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 182 citations in OpenAlex.
- Accuracy of Deep Learning in Diagnosing Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled 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
- Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.BMC medical informatics and decision making · 2025Pooled it
- A transformer-graph neural network framework for cyber twin-driven personalized respiratory monitoring.Scientific reports · 2026Article
- A Robust Deep Learning Approach for COPD Automated Detection.Sensors (Basel, Switzerland) · 2026Article
- Novel deep learning-based optimization framework for the classification of respiratory diseases using lung sound analysis.Scientific reports · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.Scientific reports · 2026Article
- Adaptive federated learning with differential privacy for multi-class respiratory disease recognition from lung sound recordings.Frontiers in digital health · 2026Article
- Artificial Intelligence Applications in Chronic Obstructive Pulmonary Disease: A Global Scoping Review of Diagnostic, Symptom-Based, and Outcome Prediction Approaches.Biomedicines · 2025Review
- Association of daily lung condition in COPD patients with wearable speech and physiological data.Scientific reports · 2025Article
- Optimizing MFCC Parameters for Breathing Phase Detection.Sensors (Basel, Switzerland) · 2025Article
- A Multimodal AI Framework for Automated Multiclass Lung Disease Diagnosis from Respiratory Sounds with Simulated Biomarker Fusion and Personalized Medication Recommendation.International journal of molecular sciences · 2025Article
- Observational
- Five-Cavity Resonance Inspired, rGO Nano-Sheet Reinforced, Multi-Site Voice Synergetic Detection Hydrogel Sensors with Diverse Self-Adhesion and Robust Wireless Transmissibility.Gels (Basel, Switzerland) · 2025Article
- Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.Physiological measurement · 2025Article
- E-RespiNet: An LLM-ELECTRA driven triple-stream CNN with feature fusion for asthma classification.PloS one · 2025Article
- A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain's challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.
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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.