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Information set supported deep learning architectures for improving noisy image classification

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dc.contributor.authorBhardwaj, Saurabhen
dc.contributor.authorWang, Yizhien
dc.contributor.authorYu, Guoqiangen
dc.contributor.authorWang, Yueen
dc.date.accessioned2023-10-10T13:27:14Zen
dc.date.available2023-10-10T13:27:14Zen
dc.date.issued2023-03en
dc.description.abstractDeep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures.en
dc.description.notesAcknowledgementsThe research reported in this article was conducted at Thapar Institute of Engineering and Technology in India and Virginia Polytechnic Institute and State University in the USA. Financial support for this work was provided by the US National Institutes of Health under Grant MH110504.en
dc.description.sponsorshipState University in the USA; US National Institutes of Health [MH110504]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-023-31462-6en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.pmid36932103en
dc.identifier.urihttp://hdl.handle.net/10919/116436en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectImage classificationen
dc.subjectDeep learningen
dc.titleInformation set supported deep learning architectures for improving noisy image classificationen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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