dc.contributor.author | Pham, LD | |
dc.contributor.author | Phan, H | |
dc.contributor.author | Palaniappan, R | |
dc.contributor.author | Mertins, A | |
dc.contributor.author | Mcloughlin, I | |
dc.date.accessioned | 2021-04-08T09:09:21Z | |
dc.date.available | 2021-04-08T09:09:21Z | |
dc.date.issued | 2021-03 | |
dc.identifier.citation | Pham, Lam Dang et al. "CNN-Moe Based Framework For Classification Of Respiratory Anomalies And Lung Disease Detection". IEEE Journal Of Biomedical And Health Informatics, 2021, pp. 1-1. Institute Of Electrical And Electronics Engineers (IEEE), doi:10.1109/jbhi.2021.3064237. Accessed 8 Apr 2021. | en_US |
dc.identifier.issn | 2168-2194 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/71124 | |
dc.description.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. | en_US |
dc.format.extent | 1 - 1 | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | IEEE Journal of Biomedical and Health Informatics | |
dc.title | CNN-MoE based framework for classification of respiratory anomalies and lung disease detection | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.identifier.doi | 10.1109/jbhi.2021.3064237 | |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |