Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network
An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radi...
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| Published in | Computational intelligence and neuroscience Vol. 2022; pp. 3500552 - 7 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Hindawi
30.04.2022
John Wiley & Sons, Inc Hindawi Limited |
| Subjects | |
| Online Access | Get full text |
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| Abstract | An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms. |
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| AbstractList | An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms. |
| Audience | Academic |
| Author | AL-Ghamdi, Abdullah S. AL-Malaise Ragab, Mahmoud Koundal, Deepika Asseri, Amer H. AlGhamdi, Saad Abdulla Mansour, Romany F. |
| AuthorAffiliation | 6 Medical Doctor, King Abdulaziz General Hospital, Jeddah, Saudi Arabia 1 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia 9 School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India 5 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt 3 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia 4 Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia 2 Information Systems Department, HECI School, Dar Alhekma University, Jeddah, Saudi Arabia 8 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt 7 Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| AuthorAffiliation_xml | – name: 5 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt – name: 2 Information Systems Department, HECI School, Dar Alhekma University, Jeddah, Saudi Arabia – name: 7 Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 3 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 9 School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India – name: 1 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 4 Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 6 Medical Doctor, King Abdulaziz General Hospital, Jeddah, Saudi Arabia – name: 8 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt |
| Author_xml | – sequence: 1 givenname: Abdullah S. AL-Malaise orcidid: 0000-0001-9259-4536 surname: AL-Ghamdi fullname: AL-Ghamdi, Abdullah S. AL-Malaise organization: Information Systems DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabiakau.edu.sa – sequence: 2 givenname: Mahmoud orcidid: 0000-0002-4427-0016 surname: Ragab fullname: Ragab, Mahmoud organization: Information Technology DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabiakau.edu.sa – sequence: 3 givenname: Saad Abdulla surname: AlGhamdi fullname: AlGhamdi, Saad Abdulla organization: Medical DoctorKing Abdulaziz General HospitalJeddahSaudi Arabia – sequence: 4 givenname: Amer H. orcidid: 0000-0003-0794-4664 surname: Asseri fullname: Asseri, Amer H. organization: Centre for Artificial Intelligence in Precision MedicinesKing Abdulaziz UniversityJeddah 21589Saudi Arabiakau.edu.sa – sequence: 5 givenname: Romany F. orcidid: 0000-0001-5857-8495 surname: Mansour fullname: Mansour, Romany F. organization: Department of MathematicsFaculty of ScienceNew Valley UniversityEl-Kharga, 72511Egypt – sequence: 6 givenname: Deepika orcidid: 0000-0003-1688-8772 surname: Koundal fullname: Koundal, Deepika organization: School of Computer ScienceUniversity of Petroleum & Energy StudiesDehradunIndiaupes.ac.in |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35535186$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_7759_cureus_59799 crossref_primary_10_1007_s42044_024_00181_0 crossref_primary_10_1016_j_aej_2024_05_073 crossref_primary_10_7717_peerj_cs_1631 crossref_primary_10_1016_j_health_2023_100282 crossref_primary_10_1016_j_compbiomed_2024_108800 crossref_primary_10_1080_21681163_2023_2272976 crossref_primary_10_1159_000536277 crossref_primary_10_1016_j_neucom_2023_126629 crossref_primary_10_1111_exsy_13530 |
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| ContentType | Journal Article |
| Copyright | Copyright © 2022 Abdullah S. AL-Malaise AL-Ghamdi et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Abdullah S. AL-Malaise AL-Ghamdi et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Abdullah S. AL-Malaise AL-Ghamdi et al. 2022 |
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| References | 22 23 24 26 27 28 29 B. Zoph (34) 2017 30 31 10 32 11 33 12 13 14 15 16 17 18 19 1 Ö. Çiçek (25) 2016 2 3 4 5 6 7 8 9 20 21 |
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| SubjectTerms | Accuracy Algorithms Artificial neural networks Computers Datasets Deep learning Dental caries Dental disorders Dentistry Dentists Diabetic retinopathy Humans Image classification Medical imaging Medical imaging equipment Model accuracy Neural networks Neural Networks, Computer Radiographs Radiography Radiography, Panoramic - methods Stomatognathic Diseases Teeth X-Rays |
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| Title | Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network |
| URI | https://dx.doi.org/10.1155/2022/3500552 https://www.ncbi.nlm.nih.gov/pubmed/35535186 https://www.proquest.com/docview/2660748606 https://search.proquest.com/docview/2661953523 https://pubmed.ncbi.nlm.nih.gov/PMC9078756 |
| Volume | 2022 |
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