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Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning
Background and Objective: The tongue mucosa often changes due to various local and systemic diseases or conditions. This study aimed to investigate whether deep learning can help detect abnormal regions on the dorsal tongue surface in patients and healthy adults. Materials and Methods: The study col...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385577/ https://www.ncbi.nlm.nih.gov/pubmed/37512104 http://dx.doi.org/10.3390/medicina59071293 |
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author | Song, Ho-Jun Park, Yeong-Joon Jeong, Hie-Yong Kim, Byung-Gook Kim, Jae-Hyung Im, Yeong-Gwan |
author_facet | Song, Ho-Jun Park, Yeong-Joon Jeong, Hie-Yong Kim, Byung-Gook Kim, Jae-Hyung Im, Yeong-Gwan |
author_sort | Song, Ho-Jun |
collection | PubMed |
description | Background and Objective: The tongue mucosa often changes due to various local and systemic diseases or conditions. This study aimed to investigate whether deep learning can help detect abnormal regions on the dorsal tongue surface in patients and healthy adults. Materials and Methods: The study collected 175 clinical photographic images of the dorsal tongue surface, which were divided into 7782 cropped images classified into normal, abnormal, and non-tongue regions and trained using the VGG16 deep learning model. The 80 photographic images of the entire dorsal tongue surface were used for the segmentation of abnormal regions using point mapping segmentation. Results: The F1-scores of the abnormal and normal classes were 0.960 (precision: 0.935, recall: 0.986) and 0.968 (precision: 0.987, recall: 0.950), respectively, in the prediction of the VGG16 model. As a result of evaluation using point mapping segmentation, the average F1-scores were 0.727 (precision: 0.717, recall: 0.737) and 0.645 (precision: 0.650, recall: 0.641), the average intersection of union was 0.695 and 0.590, and the average precision was 0.940 and 0.890, respectively, for abnormal and normal classes. Conclusions: The deep learning algorithm used in this study can accurately determine abnormal areas on the dorsal tongue surface, which can assist in diagnosing specific diseases or conditions of the tongue mucosa. |
format | Online Article Text |
id | pubmed-10385577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103855772023-07-30 Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning Song, Ho-Jun Park, Yeong-Joon Jeong, Hie-Yong Kim, Byung-Gook Kim, Jae-Hyung Im, Yeong-Gwan Medicina (Kaunas) Article Background and Objective: The tongue mucosa often changes due to various local and systemic diseases or conditions. This study aimed to investigate whether deep learning can help detect abnormal regions on the dorsal tongue surface in patients and healthy adults. Materials and Methods: The study collected 175 clinical photographic images of the dorsal tongue surface, which were divided into 7782 cropped images classified into normal, abnormal, and non-tongue regions and trained using the VGG16 deep learning model. The 80 photographic images of the entire dorsal tongue surface were used for the segmentation of abnormal regions using point mapping segmentation. Results: The F1-scores of the abnormal and normal classes were 0.960 (precision: 0.935, recall: 0.986) and 0.968 (precision: 0.987, recall: 0.950), respectively, in the prediction of the VGG16 model. As a result of evaluation using point mapping segmentation, the average F1-scores were 0.727 (precision: 0.717, recall: 0.737) and 0.645 (precision: 0.650, recall: 0.641), the average intersection of union was 0.695 and 0.590, and the average precision was 0.940 and 0.890, respectively, for abnormal and normal classes. Conclusions: The deep learning algorithm used in this study can accurately determine abnormal areas on the dorsal tongue surface, which can assist in diagnosing specific diseases or conditions of the tongue mucosa. MDPI 2023-07-13 /pmc/articles/PMC10385577/ /pubmed/37512104 http://dx.doi.org/10.3390/medicina59071293 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Song, Ho-Jun Park, Yeong-Joon Jeong, Hie-Yong Kim, Byung-Gook Kim, Jae-Hyung Im, Yeong-Gwan Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning |
title | Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning |
title_full | Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning |
title_fullStr | Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning |
title_full_unstemmed | Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning |
title_short | Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning |
title_sort | detection of abnormal changes on the dorsal tongue surface using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385577/ https://www.ncbi.nlm.nih.gov/pubmed/37512104 http://dx.doi.org/10.3390/medicina59071293 |
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