Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/23979
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, J | - |
dc.contributor.author | Wang, C | - |
dc.contributor.author | Chen, J | - |
dc.contributor.author | Zhang, H | - |
dc.contributor.author | Dai, Y | - |
dc.contributor.author | Wang, L | - |
dc.contributor.author | Wang, L | - |
dc.contributor.author | Nandi, AK | - |
dc.date.accessioned | 2022-01-22T08:15:42Z | - |
dc.date.available | 2022-01-22T08:15:42Z | - |
dc.date.issued | 2022-01-21 | - |
dc.identifier | ORCID iDs: Jianqiang Li https://orcid.org/0000-0002-2208-962X; Cheng Wang https://orcid.org/0000-0002-6043-2150; Jie Chen https://orcid.org/0000-0002-9811-1694; Li Wang https://orcid.org/0000-0001-8707-3269; Asoke K. Nandi https://orcid.org/0000-0001-6248-2875. | - |
dc.identifier.citation | Li, J. et al. (2022) 'Explainable CNN with Fuzzy Tree Regularization for Respiratory Sound Analysis', IEEE Transactions on Fuzzy Systems, 30 (6), pp. 1516 - 1528. doi: 10.1109/TFUZZ.2022.3144448. | en_US |
dc.identifier.issn | 1063-6706 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23979 | - |
dc.description.abstract | Copyright © The Author(s) 2022. Auscultation is an important tool for diagnosing respiratory-related diseases. Unfortunately, the quality of auscultation is limited by the professional level of the doctor and the environment of the auscultation. Some studies have focused on automated auscultation techniques. However, existing approaches suffer from two challenges: 1) the models cannot learn from data distributed among multiple hospitals and 2) the predictions of the models are difficult to interpret for physicians. To address this issue, this article proposes a novel explainable respiratory sound analysis framework with fuzzy decision tree regularization. This framework develops an ensemble knowledge distillation technique to learn distributed data and achieves good performance in terms of model efficiency and accuracy. Fuzzy decision trees are used to explain the predictions of the model and produce decision rules that can be well accepted by physicians. The effectiveness of this framework is thoroughly validated on the Respiratory Sound database and compared with other existing approaches. | - |
dc.description.sponsorship | National Key R&D Program of China (Grant Number: 2020YFA0908700); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62073225, 62072315, 61836005 and 62006157); 10.13039/501100003453-Natural Science Foundation of Guangdong Province (Grant Number: 2019B151502018); 10.13039/100016691-Guangdong Provincial Pearl River Talents Program (Grant Number: 2019ZT08X603); Shenzhen Science and Technology Program (Grant Number: JCYJ20210324093808021); 10.13039/501100010877-Shenzhen Science and Technology Innovation Commission (Grant Number: R2020A045), | - |
dc.format.extent | 1516 - 1528 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © The Author(s) 2022. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | respiratory sounds | en_US |
dc.subject | interpretable | en_US |
dc.subject | fuzzy decision tree | en_US |
dc.subject | convolutional neural network | en_US |
dc.subject | knowledge distillation | en_US |
dc.title | Explainable CNN with fuzzy tree regularization for respiratory sound analysis | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TFUZZ.2022.3144448 | - |
dc.relation.isPartOf | IEEE Transactions on Fuzzy Systems | - |
pubs.issue | 6 | - |
pubs.publication-status | Published | - |
pubs.volume | 30 | - |
dc.identifier.eissn | 1941-0034 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © The Author(s) 2022. Published by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | 5.39 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License