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CNN-MoE based framework for classification of respiratory anomalies and lung disease detection

Pham, Lam Dang, Phan, Huy, Palaniappan, Ramaswamy, Mertins, Alfred, McLoughlin, Ian Vince (2021) CNN-MoE based framework for classification of respiratory anomalies and lung disease detection. IEEE Journal of Biomedical and Health Informatics, 25 (8). pp. 2938-2947. ISSN 2168-2194. (doi:10.1109/jbhi.2021.3064237) (KAR id:91393)

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Official URL:
https://doi.org/10.1109/jbhi.2021.3064237

Abstract

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from respiratory sound recordings. The framework begins with front-end feature extraction that transforms input sound into a spectrogram representation. Then, a back-end deep learning network is used to classify the spectrogram features into categories of respiratory anomaly cycles or diseases. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, confirm three main contributions towards respiratory- sound analysis. Firstly, we carry out an extensive exploration of the effect of spectrogram types, spectral-time resolution, overlapping/non-overlapping windows, and data augmentation on final prediction accuracy. This leads us to propose a novel deep learning system, built on the proposed framework, which outperforms current state-of-the-art methods. Finally, we apply a Teacher-Student scheme to achieve a trade-off between model performance and model complexity which holds promise for building real-time applications.

Item Type: Article
DOI/Identification number: 10.1109/jbhi.2021.3064237
Subjects: R Medicine > R Medicine (General) > R858 Computer applications to medicine. Medical informatics. Medical information technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Palaniappan Ramaswamy
Date Deposited: 07 Nov 2021 11:55 UTC
Last Modified: 07 Nov 2021 11:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91393 (The current URI for this page, for reference purposes)
Phan, Huy: https://orcid.org/0000-0003-4096-785X
Palaniappan, Ramaswamy: https://orcid.org/0000-0001-5296-8396
McLoughlin, Ian Vince: https://orcid.org/0000-0001-7111-2008
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