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dc.contributor.authorZhang, Tianxiao
dc.contributor.authorBur, Andrés M.
dc.contributor.authorKraft, Shannon
dc.contributor.authorKavookjian, Hannah
dc.contributor.authorRenslo, Bryan
dc.contributor.authorChen, Xiangyu
dc.contributor.authorLuo, Bo
dc.contributor.authorWang, Guanghui
dc.date.accessioned2023-07-11T14:11:33Z
dc.date.available2023-07-11T14:11:33Z
dc.date.issued2023-05-29
dc.identifier.citationZhang, T.; Bur, A.M.; Kraft, S.; Kavookjian, H.; Renslo, B.; Chen, X.; Luo, B.; Wang, G. Gender, Smoking History, and Age Prediction from Laryngeal Images. J. Imaging 2023, 9, 109. https://doi.org/10.3390/jimaging9060109en_US
dc.identifier.urihttps://hdl.handle.net/1808/34579
dc.description.abstractFlexible laryngoscopy is commonly performed by otolaryngologists to detect laryngeal diseases and to recognize potentially malignant lesions. Recently, researchers have introduced machine learning techniques to facilitate automated diagnosis using laryngeal images and achieved promising results. The diagnostic performance can be improved when patients’ demographic information is incorporated into models. However, the manual entry of patient data is time-consuming for clinicians. In this study, we made the first endeavor to employ deep learning models to predict patient demographic information to improve the detector model’s performance. The overall accuracy for gender, smoking history, and age was 85.5%, 65.2%, and 75.9%, respectively. We also created a new laryngoscopic image set for the machine learning study and benchmarked the performance of eight classical deep learning models based on CNNs and Transformers. The results can be integrated into current learning models to improve their performance by incorporating the patient’s demographic information.en_US
dc.publisherMDPIen_US
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectLaryngeal imagesen_US
dc.subjectCAMen_US
dc.subjectGenderen_US
dc.subjectSmoking historyen_US
dc.subjectDemographic informationen_US
dc.titleGender, Smoking History, and Age Prediction from Laryngeal Imagesen_US
dc.typeArticleen_US
kusw.kuauthorZhang, Tianxiao
kusw.kuauthorChen, Xiangyu
kusw.kuauthorLuo, Bo
kusw.kudepartmentElectrical Engineering and Computer Scienceen_US
dc.identifier.doi10.3390/jimaging9060109en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6171-3176en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6879-6453en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-9506-8902en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-9690-0067en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-3182-104Xen_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.identifier.pmidPMC10301395en_US
dc.rights.accessrightsopenAccessen_US


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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.