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Gender, Smoking History, and Age Prediction from Laryngeal Images
dc.contributor.author | Zhang, Tianxiao | |
dc.contributor.author | Bur, Andrés M. | |
dc.contributor.author | Kraft, Shannon | |
dc.contributor.author | Kavookjian, Hannah | |
dc.contributor.author | Renslo, Bryan | |
dc.contributor.author | Chen, Xiangyu | |
dc.contributor.author | Luo, Bo | |
dc.contributor.author | Wang, Guanghui | |
dc.date.accessioned | 2023-07-11T14:11:33Z | |
dc.date.available | 2023-07-11T14:11:33Z | |
dc.date.issued | 2023-05-29 | |
dc.identifier.citation | Zhang, 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/jimaging9060109 | en_US |
dc.identifier.uri | https://hdl.handle.net/1808/34579 | |
dc.description.abstract | Flexible 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.publisher | MDPI | en_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.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.subject | Laryngeal images | en_US |
dc.subject | CAM | en_US |
dc.subject | Gender | en_US |
dc.subject | Smoking history | en_US |
dc.subject | Demographic information | en_US |
dc.title | Gender, Smoking History, and Age Prediction from Laryngeal Images | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Zhang, Tianxiao | |
kusw.kuauthor | Chen, Xiangyu | |
kusw.kuauthor | Luo, Bo | |
kusw.kudepartment | Electrical Engineering and Computer Science | en_US |
dc.identifier.doi | 10.3390/jimaging9060109 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-6171-3176 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-6879-6453 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-9506-8902 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-9690-0067 | en_US |
dc.identifier.orcid | https://orcid.org/0000-0003-3182-104X | en_US |
kusw.oaversion | Scholarly/refereed, publisher version | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.identifier.pmid | PMC10301395 | en_US |
dc.rights.accessrights | openAccess | en_US |