Robust respiratory disease classification using breathing sounds (RRDCBS) multiple features and models

Classification of respiratory diseases using X-ray and CT scan images of lungs is currently practised and used by many medical practitioners for clinical diagnosis. Respiratory disease classification, using breathing and wheezing sounds, remains scarce in the research field and is slowly upcoming. I...

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Bibliographic Details
Published in:Neural computing & applications 2022-05, Vol.34 (10), p.8155-8172
Main Authors: Revathi, A., Sasikaladevi, N., Arunprasanth, D., Amirtharajan, Rengarajan
Format: Article
Language:English
Subjects:
Acoustics
Artificial Intelligence
Artificial neural networks
Breathing
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computed tomography
Computer Science
Data Mining and Knowledge Discovery
Feature extraction
Image classification
Image Processing and Computer Vision
Modelling
Original Article
Probability and Statistics in Computer Science
Respiratory diseases
Robustness
Support vector machines
Vector quantization
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Summary:Classification of respiratory diseases using X-ray and CT scan images of lungs is currently practised and used by many medical practitioners for clinical diagnosis. Respiratory disease classification, using breathing and wheezing sounds, remains scarce in the research field and is slowly upcoming. In this work, robust respiratory disease classification using breathing sounds ( RRDCBS) is implemented by extracting multiple features from sounds, creating multiple modelling techniques, and experimental identification of diseases using appropriate testing procedures for multi-class and binary classification of respiratory diseases. Decision level fusion of features for Vector quantisation (VQ) modelling technique has provided 100% accuracy for classifying five respiratory diseases and healthy subjects. Decision level fusion of indices on the features has provided 100% accuracy for VQ, support vector machine (SVM), and K-nearest neighbour (KNN) modelling techniques to perform binary classification of the respiratory disease against healthy data sound. Deep recurrent and convolutional neural networks are also evaluated for multiple/binary classification of respiratory diseases.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-06915-0