Pham, Lam Dang, McLoughlin, Ian Vince, Phan, Huy, Nguyen, Truc, Palaniappan, Ramaswamy (2020) Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). . (doi:10.1109/EMBC44109.2020.9175704) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91414)
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Official URL: https://ieeexplore.ieee.org/abstract/document/9175... |
Abstract
This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete detection process firstly involves front end feature extraction where recordings are transformed into spectrograms that convey both spectral and temporal information. Then a back-end deep learning model classifies the features into classes of respiratory disease or anomaly. Experiments, conducted over the ICBHI benchmark dataset of respiratory sounds, evaluate the ability of the framework to classify sounds. Two main contributions are made in this paper. Firstly, we provide an extensive analysis of how factors such as respiratory cycle length, time resolution, and network architecture, affect final prediction accuracy. Secondly, a novel deep learning based framework is proposed for detection of respiratory diseases and shown to perform extremely well compared to state of the art methods.
Item Type: | Conference or workshop item (Proceeding) |
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DOI/Identification number: | 10.1109/EMBC44109.2020.9175704 |
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: | 08 Nov 2021 10:56 UTC |
Last Modified: | 04 Mar 2024 15:30 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/91414 (The current URI for this page, for reference purposes) |
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