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

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91393 CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
sameAs	CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
http://eprints.org/ontology/hasAccepted	CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (PDF)
http://eprints.org/ontology/hasDocument	CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (PDF) CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (Other) CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (Other) CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (Other) CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (Other) CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (Other)
dc:hasVersion	CNN-MoE based framework for classification of respiratory anomalies and lung disease detection (PDF)
Title	CNN-MoE based framework for classification of respiratory anomalies and lung disease detection
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-03-08
Creator	Alfred Mertins Huy Phan I. McLoughlin Lam Dang Pham Palaniappan Ramaswamy
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	IEEE Journal of Biomedical and Health Informatics https://kar.kent.ac.uk/id/repository
Subject	R858 Computer applications to medicine. Medical informatics. Medical information technology
list of authors	https://kar.kent.ac.uk/id/eprint/91393#authors
issue	8
volume	25
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91393 CNN-MoE based framework for classification of respiratory anomalies and lung disease detection

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