Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan

Dental impression tray is frequently used in dentistry to record patient’s oral structure for clinical oral diagnosis and treatment planning. Manual procedure of taking impressions is costly, time-consuming, and additionally, no research has been done to select dental impression tray from dental arc...

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Main Author: Muhammad Asif , Hasan
Format: Thesis
Published: 2021
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Online Access:http://studentsrepo.um.edu.my/14949/1/Muhammad_Asif.pdf
http://studentsrepo.um.edu.my/14949/2/Muhammad_Asif.pdf
http://studentsrepo.um.edu.my/14949/
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spelling my.um.stud.149492025-01-12T18:43:03Z Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan Muhammad Asif , Hasan QA75 Electronic computers. Computer science Dental impression tray is frequently used in dentistry to record patient’s oral structure for clinical oral diagnosis and treatment planning. Manual procedure of taking impressions is costly, time-consuming, and additionally, no research has been done to select dental impression tray from dental arch images using computer vision in real-life scenarios. In this spirit, an intelligent model is proposed based on computer vision and machine learning to select appropriate dental impression trays from maxillary arch images. A dataset of 52 patients’ maxillary arch images that matches one from 4 sizes of Kurten’s impression tray have been acquired. Various sets features such as, colors, textures, and shapes of the images were extracted to better characterize the maxillary arch images. Considering the importance of the features in describing the maxillary arch object and to improve the classification performance, a method based on multi-feature with ensemble classifier is proposed. Besides, the performance of a deep-learning based multilayer perceptron neural network is also investigated. The proposed multi-feature with ensemble classifier attained 92.31% precision, 91.75% recall, 91.75% accuracy, respectively, on the dataset. This clearly establishes the feasibility of this study. An illustration of a real-life application of the proposed model is also provided. 2021-09 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/14949/1/Muhammad_Asif.pdf application/pdf http://studentsrepo.um.edu.my/14949/2/Muhammad_Asif.pdf Muhammad Asif , Hasan (2021) Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan. Masters thesis, Universiti Malaya. http://studentsrepo.um.edu.my/14949/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Muhammad Asif , Hasan
Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan
description Dental impression tray is frequently used in dentistry to record patient’s oral structure for clinical oral diagnosis and treatment planning. Manual procedure of taking impressions is costly, time-consuming, and additionally, no research has been done to select dental impression tray from dental arch images using computer vision in real-life scenarios. In this spirit, an intelligent model is proposed based on computer vision and machine learning to select appropriate dental impression trays from maxillary arch images. A dataset of 52 patients’ maxillary arch images that matches one from 4 sizes of Kurten’s impression tray have been acquired. Various sets features such as, colors, textures, and shapes of the images were extracted to better characterize the maxillary arch images. Considering the importance of the features in describing the maxillary arch object and to improve the classification performance, a method based on multi-feature with ensemble classifier is proposed. Besides, the performance of a deep-learning based multilayer perceptron neural network is also investigated. The proposed multi-feature with ensemble classifier attained 92.31% precision, 91.75% recall, 91.75% accuracy, respectively, on the dataset. This clearly establishes the feasibility of this study. An illustration of a real-life application of the proposed model is also provided.
format Thesis
author Muhammad Asif , Hasan
author_facet Muhammad Asif , Hasan
author_sort Muhammad Asif , Hasan
title Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan
title_short Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan
title_full Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan
title_fullStr Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan
title_full_unstemmed Image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / Muhammad Asif Hasan
title_sort image based dental impression tray selection from maxillary arches using multifeature with ensemble classifier / muhammad asif hasan
publishDate 2021
url http://studentsrepo.um.edu.my/14949/1/Muhammad_Asif.pdf
http://studentsrepo.um.edu.my/14949/2/Muhammad_Asif.pdf
http://studentsrepo.um.edu.my/14949/
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score 13.244413