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Tytuł:
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Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network.
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Autorzy:
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Al-Ghamdi ASA; Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.; Information Systems Department, HECI School, Dar Alhekma University, Jeddah, Saudi Arabia.
Ragab M; Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.; Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia.; Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt.
AlGhamdi SA; Medical Doctor, King Abdulaziz General Hospital, Jeddah, Saudi Arabia.
Asseri AH; Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia.; Biochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Mansour RF; Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt.
Koundal D; School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India.
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Źródło:
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Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Apr 30; Vol. 2022, pp. 3500552. Date of Electronic Publication: 2022 Apr 30 (Print Publication: 2022).
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: New York, NY : Hindawi Pub. Corp.
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MeSH Terms:
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Neural Networks, Computer*
Stomatognathic Diseases*
Algorithms ; Humans ; Radiography, Panoramic/methods ; X-Rays
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References:
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Oral Radiol. 2021 Jan;37(1):13-19. (PMID: 31893343)
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Diagn Interv Radiol. 2021 Jan;27(1):20-27. (PMID: 32815519)
Glob Transit. 2020;2:283-292. (PMID: 33205037)
Oral Radiol. 2020 Oct;36(4):337-343. (PMID: 31535278)
Lancet Oncol. 2019 May;20(5):e253-e261. (PMID: 31044723)
Dentomaxillofac Radiol. 2019 May;48(4):20180051. (PMID: 30835551)
Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:1003-1006. (PMID: 29062466)
Int J Med Inform. 2018 Sep;117:44-54. (PMID: 30032964)
IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2481-2495. (PMID: 28060704)
Anthropol Anz. 2013;70(3):331-45. (PMID: 24466642)
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Entry Date(s):
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Date Created: 20220510 Date Completed: 20220511 Latest Revision: 20220716
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Update Code:
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20240105
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PubMed Central ID:
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PMC9078756
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DOI:
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10.1155/2022/3500552
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PMID:
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35535186
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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.
Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper.
(Copyright © 2022 Abdullah S. AL-Malaise AL-Ghamdi et al.)
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