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Texture-Based Neural Network Model for Biometric Dental Applications
Background: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. Methods: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intrao...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9781388/ https://www.ncbi.nlm.nih.gov/pubmed/36556175 http://dx.doi.org/10.3390/jpm12121954 |
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author | Saleh, Omnia Nozaki, Kosuke Matsumura, Mayuko Yanaka, Wataru Miura, Hiroyuki Fueki, Kenji |
author_facet | Saleh, Omnia Nozaki, Kosuke Matsumura, Mayuko Yanaka, Wataru Miura, Hiroyuki Fueki, Kenji |
author_sort | Saleh, Omnia |
collection | PubMed |
description | Background: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. Methods: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. Results: Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. Conclusion: The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics. |
format | Online Article Text |
id | pubmed-9781388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97813882022-12-24 Texture-Based Neural Network Model for Biometric Dental Applications Saleh, Omnia Nozaki, Kosuke Matsumura, Mayuko Yanaka, Wataru Miura, Hiroyuki Fueki, Kenji J Pers Med Article Background: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. Methods: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. Results: Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. Conclusion: The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics. MDPI 2022-11-25 /pmc/articles/PMC9781388/ /pubmed/36556175 http://dx.doi.org/10.3390/jpm12121954 Text en © 2022 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 Saleh, Omnia Nozaki, Kosuke Matsumura, Mayuko Yanaka, Wataru Miura, Hiroyuki Fueki, Kenji Texture-Based Neural Network Model for Biometric Dental Applications |
title | Texture-Based Neural Network Model for Biometric Dental Applications |
title_full | Texture-Based Neural Network Model for Biometric Dental Applications |
title_fullStr | Texture-Based Neural Network Model for Biometric Dental Applications |
title_full_unstemmed | Texture-Based Neural Network Model for Biometric Dental Applications |
title_short | Texture-Based Neural Network Model for Biometric Dental Applications |
title_sort | texture-based neural network model for biometric dental applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9781388/ https://www.ncbi.nlm.nih.gov/pubmed/36556175 http://dx.doi.org/10.3390/jpm12121954 |
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