ArticleJournal of ambient intelligence and humanized computing2022
Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory.
Article in Journal of ambient intelligence and humanized computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sound Classification and Processing of Urban Environments: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- 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
- LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.Scientific reports · 2026Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- 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
- Deep learning-based lung sound analysis for intelligent stethoscope.Military Medical Research · 2023Review
- Article
- Interpretation of lung disease classification with light attention connected module.Biomedical signal processing and control · 2023Article
- 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
- Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks.Frontiers in medicine · 2023Article
- Article
- A review on lung disease recognition by acoustic signal analysis with deep learning networks.Journal of big data · 2023Article
- Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil.International journal of environmental research and public health · 2022Article
- Enabling effective breathing sound analysis for automated diagnosis of lung diseases.Smart health (Amsterdam, Netherlands) · 2022Article
- On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- The coming era of a new auscultation system for analyzing respiratory sounds.BMC pulmonary medicine · 2022Review
- CoCross: An ICT Platform Enabling Monitoring Recording and Fusion of Clinical Information Chest Sounds and Imaging of COVID-19 ICU Patients.Healthcare (Basel, Switzerland) · 2022Article
- Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.PloS one · 2022Article
- Deep learning models for detecting respiratory pathologies from raw lung auscultation sounds.Soft computing · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, a study is conducted to explore the ability of deep learning in recognizing pulmonary diseases from electronically recorded lung sounds. The selected data-set included a total of 103 patients obtained from locally recorded stethoscope lung sounds acquired at King Abdullah University Hospital, Jordan University of Science and Technology, Jordan. In addition, 110 patients data were added to the data-set from the Int. Conf. on Biomedical Health Informatics publicly available challenge database. Initially, all signals were checked to have a sampling frequency of 4 kHz and segmented into 5 s segments. Then, several preprocessing steps were undertaken to ensure smoother and less noisy signals. These steps included wavelet smoothing, displacement artifact removal, and Supplementary Information: The online version supplementary material available at 10.1007/s12652-021-03184-y.
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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.