Lung disease recognition methods using audio-based analysis with machine learning

Heliyon, Journal Year: 2024, Volume and Issue: 10(4), P. e26218 - e26218

Published: Feb. 1, 2024

The use of computer-based automated approaches and improvements in lung sound recording techniques have made sound-based diagnostics even better devoid subjectivity errors. Using a computer to evaluate features more thoroughly with the analyzing changes behavior, measurements, suppressing presence noise contaminations, graphical representations are all possible by analysis. This paper starts discussion need for this research area, providing an overview field motivations behind it. Following that, it details survey methodology used work. It presents on elements disease classification using machine learning algorithms. includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal lung-heart separation, deep algorithms, wavelet transform audio signals. study introduces studies that review screening including summary table these references discusses literature gaps existing studies. is concluded respiratory diseases has promising results. While we believe material will prove valuable physicians researchers exploring sound-signal-based learning, large-scale investigations remain essential solidify findings foster wider adoption within medical community.

Language: Английский

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Respiratory sound classification utilizing human auditory-based feature extraction.

Physica Scripta, Journal Year: 2025, Volume and Issue: 100(4), P. 046003 - 046003

Published: Feb. 19, 2025

Abstract A major worldwide health concern is chronic respiratory diseases (CRDs), which include disorders including asthma, pulmonary hypertension, occupational lung diseases, and obstructive disease (COPD). Improving clinical results treatment efficacy requires an early precise diagnosis. In order to classify sounds, this study presents a novel framework that incorporates auditory-inspired characteristics, such as Mel-Frequency Cepstral Coefficients (MFCCs), Mel Spectrograms, Cochleograms, into CNN-LSTM architecture. The uses sophisticated feature extraction techniques in conjunction with strong data augmentation approaches address the issue of class imbalance guarantee thorough representation variety sound patterns. Using Respiratory Sound Database, suggested model was assessed showed remarkable performance, obtaining F1 score 98.94%, accuracy 98.90%, specificity 99.80%, sensitivity ICBHI 99.40%. These findings demonstrate model’s potential reliable efficient tool for identification evaluation CRDs, would significantly improve patient care management illnesses. outstanding performance further emphasizes importance settings, enabling improved conditions.

Language: Английский

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Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

Diagnostics, Journal Year: 2023, Volume and Issue: 13(10), P. 1748 - 1748

Published: May 16, 2023

Lung auscultation has long been used as a valuable medical tool to assess respiratory health and gotten lot of attention in recent years, notably following the coronavirus epidemic. is patient’s role. Modern technological progress guided growth computer-based speech investigation, for detecting lung abnormalities diseases. Several studies have reviewed this important area, but none are specific sound-based analysis with deep-learning architectures from one side provided information was not sufficient good understanding these techniques. This paper gives complete review prior deep-learning-based architecture sound analysis. Deep-learning-based articles found different databases including Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, IEEE. More than 160 publications were extracted submitted assessment. discusses trends pathology/lung sound, common features classifying sounds, several considered datasets, classification methods, signal processing techniques, some statistical based on previous study findings. Finally, assessment concludes discussion potential future improvements recommendations.

Language: Английский

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Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers

Sensors, Journal Year: 2024, Volume and Issue: 24(2), P. 682 - 682

Published: Jan. 21, 2024

Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis sounds plays a significant role in characterizing the system's condition identifying abnormalities. main contribution this study to investigate performance when input data, represented by cochleogram, used feed Vision Transformer architecture, since classifier combination first time it has been applied adventitious sound classification our knowledge. Although ViT shown promising results audio tasks applying self attention spectrogram patches, we extend approach which captures specific spectro-temporal features sounds. proposed methodology evaluated on ICBHI dataset. We compare with other state art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant Q transform, cochleogram as data. Our confirm superior combining ViT, highlighting potential reliable classification. This contributes ongoing efforts developing automatic intelligent techniques aim significantly augment speed effectiveness disease detection, thereby addressing need medical field.

Language: Английский

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Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning

Bioengineering, Journal Year: 2024, Volume and Issue: 11(6), P. 586 - 586

Published: June 8, 2024

Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) essential for effective management respiratory diseases. However, interpretation sounds is subjective labor-intensive process that demands considerable medical expertise, there good chance misclassification. To address this problem, we propose hybrid deep learning technique incorporates signal processing techniques. Parallel transformation applied to adventitious sounds, transforming sound signals into two distinct time-frequency scalograms: continuous wavelet transform mel spectrogram. Furthermore, parallel convolutional autoencoders employed extract features from scalograms, resulting latent space fused feature pool. Finally, leveraging long short-term memory model, used as input classifying Our work evaluated using ICBHI-2017 dataset. The experimental findings indicate our proposed method achieves promising predictive performance, average values accuracy, sensitivity, specificity, F1-score 94.16%, 89.56%, 99.10%, respectively, eight-class diseases; 79.61%, 78.55%, 92.49%, 78.67%, four-class 85.61%, 83.44%, 84.21%, binary-class (normal vs. abnormal) sounds.

Language: Английский

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Artificial intelligence for accurate classification of respiratory abnormality levels using image-based features and interpretable insights

Applied Soft Computing, Journal Year: 2025, Volume and Issue: 170, P. 112678 - 112678

Published: Jan. 9, 2025

Language: Английский

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A comprehensive review of computerized respiratory sound analysis and deep learning techniques for acoustic signal-based disease classification

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3281, P. 040035 - 040035

Published: Jan. 1, 2025

Language: Английский

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A comprehensive approach for high intensity blood flow signal classification using multi-scale spectrogram fusion and tunable Q-factor wavelet analysis

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 109, P. 107991 - 107991

Published: May 21, 2025

Language: Английский

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Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 171, P. 108190 - 108190

Published: Feb. 20, 2024

Language: Английский

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Scalable serial hardware architecture of multilayer perceptron neural network for automatic wheezing detection

Microprocessors and Microsystems, Journal Year: 2023, Volume and Issue: 99, P. 104844 - 104844

Published: April 28, 2023

Language: Английский

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