Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm

Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD s...

Full description

Saved in:

Bibliographic Details
Published in:	Expert systems Vol. 41; no. 6
Main Authors:	Awari, Harshavardhan, Subramani, Neelakandan, Janagaraj, Avanija, Balasubramaniapillai Thanammal, Geetha, Thangarasu, Jackulin, Kohar, Rachna
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.06.2024
Subjects:	Accuracy Algorithms Classification computer vision Datasets Deep learning dental models Dentistry Feature extraction Image classification Image filters Image segmentation Machine learning Model accuracy Orthodontics Teeth Three dimensional models tooth type classification
ISSN:	0266-4720, 1468-0394
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.
AbstractList	Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.
Author	Subramani, Neelakandan Janagaraj, Avanija Thangarasu, Jackulin Awari, Harshavardhan Balasubramaniapillai Thanammal, Geetha Kohar, Rachna
Author_xml	– sequence: 1 givenname: Harshavardhan surname: Awari fullname: Awari, Harshavardhan organization: VNR Vignana Jyothi Institute of Engineering and Technology – sequence: 2 givenname: Neelakandan orcidid: 0000-0001-8583-0019 surname: Subramani fullname: Subramani, Neelakandan email: snksnk17@gmail.com organization: R.M.K Engineering College – sequence: 3 givenname: Avanija surname: Janagaraj fullname: Janagaraj, Avanija organization: Sree Vidyanikethan Engineering College – sequence: 4 givenname: Geetha surname: Balasubramaniapillai Thanammal fullname: Balasubramaniapillai Thanammal, Geetha organization: Saveetha University – sequence: 5 givenname: Jackulin surname: Thangarasu fullname: Thangarasu, Jackulin organization: Panimalar Engineering College – sequence: 6 givenname: Rachna surname: Kohar fullname: Kohar, Rachna organization: Bennett University
BookMark	eNp9kM9KAzEQxoNUsK1efIIFb8LWpEmT7FFK_QMFD1bQ05JmJ23KbrYmW2pvPoLP6JOYdT2JOJdhZn7fB_MNUM_VDhA6J3hEYl3BWziMCCWZPEJ9wrhMMc1YD_XxmPOUiTE-QYMQNhhjIgTvI79Ye4DP94_CVuCCrZ0qkwJcE5ut1AqSAKuqnZt4S5QrEl2qEKyxulvtgnWrKIFtUoLyrp32tlknzc61THTYK18lqlzVPu6rU3RsVBng7KcP0dPNbDG9S-cPt_fT63mqKSYyzRifMANSCsIIcDCGLPlYCEnxUlOtecEnSypAET6mUDBJTYGFkZxhzSeG0SG66Hy3vn7dQWjyTb3z8b-QU8wymVHBZaRwR2lfh-DB5Np2zzZe2TInOG-Tzdtk8-9ko-Tyl2TrY1b-8DdMOnhvSzj8Q-az58eXTvMFOVKPZg
CitedBy_id	crossref_primary_10_1109_ACCESS_2025_3556523 crossref_primary_10_3390_diagnostics13152512 crossref_primary_10_3390_electronics11244178 crossref_primary_10_3390_app13010468
Cites_doi	10.1016/j.measurement.2021.109804 10.1016/j.gmod.2020.101071 10.1177/00220345211005338 10.1109/ICDE51399.2021.00319 10.1016/j.jdent.2021.103865 10.3390/s21041302 10.1016/j.cmpb.2021.106295 10.1109/ACCESS.2019.2924262 10.1109/CVPR.2019.00653 10.32604/iasc.2022.019117 10.1016/j.imavis.2022.104404 10.1007/s11042-019-7233-0 10.3390/ijerph17124424 10.1109/ISBI45749.2020.9098542 10.1117/12.2582205 10.1080/03772063.2021.1967793 10.1109/ACCESS.2021.3072336 10.1109/TVCG.2018.2839685 10.1007/s12539-021-00467-y 10.3390/s19183904 10.1007/978-3-030-86159-9_32 10.1007/978-3-030-61056-2_12 10.1016/j.eswa.2022.116968 10.1016/j.joen.2021.09.009 10.1007/978-3-030-59719-1_68
ContentType	Journal Article
Copyright	2022 John Wiley & Sons Ltd. 2024 John Wiley & Sons, Ltd.
Copyright_xml	– notice: 2022 John Wiley & Sons Ltd. – notice: 2024 John Wiley & Sons, Ltd.
DBID	AAYXX CITATION 7SC 7TB 8FD F28 FR3 JQ2 L7M L~C L~D
DOI	10.1111/exsy.13198
DatabaseName	CrossRef Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional
DatabaseTitle	CrossRef Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering Computer and Information Systems Abstracts Professional
DatabaseTitleList	CrossRef Technology Research Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science Dentistry
EISSN	1468-0394
EndPage	n/a
ExternalDocumentID	10_1111_exsy_13198 EXSY13198
Genre	article
GroupedDBID	-~X .3N .4S .DC .GA .Y3 05W 0B8 0R~ 10A 1OB 1OC 29G 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6TJ 702 77K 7PT 8-0 8-1 8-3 8-4 8-5 8UM 8VB 930 9M8 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABDPE ABEML ABLJU ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACFBH ACGFS ACIWK ACNCT ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADMHC ADNMO ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AEMOZ AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AHEFC AHQJS AI. AITYG AIURR AIWBW AJBDE AJXKR AKVCP ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CAG COF CS3 CWDTD D-E D-F DC6 DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBA EBR EBS EBU EDO EJD EMK EST ESX F00 F01 F04 FEDTE FZ0 G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K1G K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MK~ MRFUL MRSTM MSFUL MSSTM MVM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QWB R.K RIG RIWAO RJQFR ROL RX1 SAMSI SUPJJ TAE TH9 TN5 TUS UB1 VH1 W8V W99 WBKPD WH7 WIH WIK WLBEL WOHZO WQJ WRC WXSBR WYISQ XG1 ZL0 ZZTAW ~02 ~IA ~WT 77I AAMMB AAYXX ADMLS AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY AIQQE CITATION O8X 7SC 7TB 8FD F28 FR3 JQ2 L7M L~C L~D
ID	FETCH-LOGICAL-c3018-94654fe887141e6eff1b6277830bc3cc6d65b37ea1623ed483fd07f8640c65f43
IEDL.DBID	DRFUL
ISICitedReferencesCount	9
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000890240300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0266-4720
IngestDate	Sat Jul 19 20:41:04 EDT 2025 Sat Nov 29 03:32:47 EST 2025 Tue Nov 18 21:07:09 EST 2025 Wed Jan 22 17:19:15 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	6
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c3018-94654fe887141e6eff1b6277830bc3cc6d65b37ea1623ed483fd07f8640c65f43
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-8583-0019
PQID	3049893768
PQPubID	32130
PageCount	18
ParticipantIDs	proquest_journals_3049893768 crossref_citationtrail_10_1111_exsy_13198 crossref_primary_10_1111_exsy_13198 wiley_primary_10_1111_exsy_13198_EXSY13198
PublicationCentury	2000
PublicationDate	June 2024 2024-06-00 20240601
PublicationDateYYYYMMDD	2024-06-01
PublicationDate_xml	– month: 06 year: 2024 text: June 2024
PublicationDecade	2020
PublicationPlace	Oxford
PublicationPlace_xml	– name: Oxford
PublicationTitle	Expert systems
PublicationYear	2024
Publisher	Blackwell Publishing Ltd
Publisher_xml	– name: Blackwell Publishing Ltd
References	2021; 9 2021; 14 2021; 47 2022; 199 2021; 208 2019; 7 2022; 121 2021; 21 2021 2021; 115 2021; 11596 2020 2020; 17 2019 2019; 19 2021; 183 2020; 79 2022; 31 2021; 100 2020; 109 2018; 25 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_11_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_25_1 e_1_2_7_24_1 e_1_2_7_23_1 e_1_2_7_22_1 e_1_2_7_21_1 e_1_2_7_20_1
References_xml	– volume: 47 start-page: 1907 issue: 12 year: 2021 end-page: 1916 article-title: A deep learning approach to segment and classify C‐shaped canal morphologies in mandibular second molars using cone‐beam computed tomography publication-title: Journal of Endodontics – volume: 79 start-page: 15381 issue: 21 year: 2020 end-page: 15396 article-title: Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images publication-title: Multimedia Tools and Applications – volume: 14 start-page: 113 year: 2021 end-page: 129 article-title: Deep learning based capsule neural network model for breast cancer diagnosis using mammogram images publication-title: Interdisciplinary Sciences: Computational Life Sciences – start-page: 1 year: 2021 end-page: 17 article-title: Dental image segmentation and classification using inception Resnetv2 publication-title: IETE Journal of Research – volume: 11596 year: 2021 – start-page: 440 year: 2021 end-page: 454 – volume: 19 start-page: 3904 issue: 18 year: 2019 article-title: Estimation of arsenic content in soil based on laboratory and field reflectance spectroscopy publication-title: Sensors – start-page: 145 year: 2020 end-page: 153 – volume: 208 year: 2021 article-title: Hierarchical CNN‐based occlusal surface morphology analysis for classifying posterior tooth type using augmented images from 3D dental surface models publication-title: Computer Methods and Programs in Biomedicine – start-page: 939 year: 2020 end-page: 942 – volume: 9 start-page: 56066 year: 2021 end-page: 56092 article-title: An improved tunicate swarm algorithm for global optimization and image segmentation publication-title: IEEE Access – start-page: 6368 year: 2019 end-page: 6377 – volume: 199 year: 2022 article-title: Progress in deep learning‐based dental and maxillofacial image analysis: A systematic review publication-title: Expert Systems with Applications – volume: 25 start-page: 2336 issue: 7 year: 2018 end-page: 2348 article-title: 3D tooth segmentation and labeling using deep convolutional neural networks publication-title: IEEE Transactions on Visualization and Computer Graphics – volume: 17 start-page: 4424 issue: 12 year: 2020 article-title: Current applications, opportunities, and limitations of AI for 3D imaging in dental research and practice publication-title: International Journal of Environmental Research and Public Health – volume: 183 year: 2021 article-title: Interpretable filter based convolutional neural network (IF‐CNN) for glucose prediction and classification using PD‐SS algorithm publication-title: Measurement – volume: 31 start-page: 621 issue: 1 year: 2022 end-page: 634 article-title: Deep learning‐based skin lesion diagnosis model using Dermoscopic images publication-title: Intelligent Automation & Soft Computing – volume: 21 start-page: 1302 issue: 4 year: 2021 article-title: A low‐cost three‐dimensional DenseNet neural network for Alzheimer's disease early discovery publication-title: Sensors – start-page: 2750 year: 2021 end-page: 2755 – volume: 100 start-page: 943 issue: 9 year: 2021 end-page: 949 article-title: Multiclass CBCT image segmentation for orthodontics with deep learning publication-title: Journal of Dental Research – start-page: 703 year: 2020 end-page: 712 – volume: 7 start-page: 84817 year: 2019 end-page: 84828 article-title: Automatic classification and segmentation of teeth on 3D dental model using hierarchical deep learning networks publication-title: IEEE Access – volume: 109 year: 2020 article-title: Automatic 3D tooth segmentation using convolutional neural networks in harmonic parameter space publication-title: Graphical Models – volume: 115 year: 2021 article-title: A novel deep learning system for multi‐class tooth segmentation and classification on cone beam computed tomography: A validation study publication-title: Journal of Dentistry – volume: 121 year: 2022 article-title: Geetha, Aditya Kumar Singh Pundir, Vinay Kumar, intelligent deep learning based ethnicity recognition and classification using facial images publication-title: Image and Vision Computing – ident: e_1_2_7_11_1 doi: 10.1016/j.measurement.2021.109804 – ident: e_1_2_7_26_1 doi: 10.1016/j.gmod.2020.101071 – ident: e_1_2_7_22_1 doi: 10.1177/00220345211005338 – ident: e_1_2_7_9_1 doi: 10.1109/ICDE51399.2021.00319 – ident: e_1_2_7_16_1 doi: 10.1016/j.jdent.2021.103865 – ident: e_1_2_7_19_1 doi: 10.3390/s21041302 – ident: e_1_2_7_4_1 doi: 10.1016/j.cmpb.2021.106295 – ident: e_1_2_7_21_1 doi: 10.1109/ACCESS.2019.2924262 – ident: e_1_2_7_5_1 doi: 10.1109/CVPR.2019.00653 – ident: e_1_2_7_15_1 doi: 10.32604/iasc.2022.019117 – ident: e_1_2_7_7_1 doi: 10.1016/j.imavis.2022.104404 – ident: e_1_2_7_13_1 doi: 10.1007/s11042-019-7233-0 – ident: e_1_2_7_10_1 doi: 10.3390/ijerph17124424 – ident: e_1_2_7_24_1 doi: 10.1109/ISBI45749.2020.9098542 – ident: e_1_2_7_3_1 doi: 10.1117/12.2582205 – ident: e_1_2_7_14_1 doi: 10.1080/03772063.2021.1967793 – ident: e_1_2_7_8_1 doi: 10.1109/ACCESS.2021.3072336 – ident: e_1_2_7_25_1 doi: 10.1109/TVCG.2018.2839685 – ident: e_1_2_7_12_1 doi: 10.1007/s12539-021-00467-y – ident: e_1_2_7_23_1 doi: 10.3390/s19183904 – ident: e_1_2_7_2_1 doi: 10.1007/978-3-030-86159-9_32 – ident: e_1_2_7_6_1 doi: 10.1007/978-3-030-61056-2_12 – ident: e_1_2_7_18_1 doi: 10.1016/j.eswa.2022.116968 – ident: e_1_2_7_17_1 doi: 10.1016/j.joen.2021.09.009 – ident: e_1_2_7_20_1 doi: 10.1007/978-3-030-59719-1_68
SSID	ssj0001776
Score	2.3776026
Snippet	Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental...
SourceID	proquest crossref wiley
SourceType	Aggregation Database Enrichment Source Index Database Publisher
SubjectTerms	Accuracy Algorithms Classification computer vision Datasets Deep learning dental models Dentistry Feature extraction Image classification Image filters Image segmentation Machine learning Model accuracy Orthodontics Teeth Three dimensional models tooth type classification
Title	Three‐dimensional dental image segmentation and classification using deep learning with tunicate swarm algorithm
URI	https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fexsy.13198 https://www.proquest.com/docview/3049893768
Volume	41
WOSCitedRecordID	wos000890240300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1468-0394 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001776 issn: 0266-4720 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB509eDFt_gmoBeFSrtNkxa8iLp4EBEfsJ5Kmk6XBbdKu75u_gR_o7_ESZuuCiKIt5ImoWQe-ZLOfAOwHbgcEXXkBImf0AFFcUd5UjgY0V6RJYGfhFlVbEKenYXdbnQ-BvtNLkzNDzG6cDOWUflrY-AqKb8YOT6XL3seaVA4DhNtUlzegomji8716cgTe7IqLkfHDOFw2XYtPamJ5Pkc_X1D-kSZX7Fqtdl0Zv73mbMwbUEmO6i1Yg7GMJ-HmaaAA7P2vADFFUkS31_fUsPxX_NzsLRKkGT9AXkaVmJvYLOTcqbylGmDtk14Ud1kwuZ7NATvma0_0WPmapcN66wTmuFJFQOmbnt3BbUPFuG6c3x1eOLYIgyOJtsnZ2gI1zIkX-RxDwVmmZeItpSh7yba11qkgsQsUXkEpDDloZ-lrsxCwV0tgoz7S9DK73JcBoaGPk1x5QeYcBQyUoGvBfe0jsKIoN4K7DSSiLVlKDeFMm7j5qRiFjOuFnMFtkZ972tejh97rTcCja1tlrH5sWhQmqDXu5XofpkhPu5e3lRPq3_pvAZTbUI_dUzZOrSGxQNuwKR-HPbLYtPq6QcWp_A8
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ba9swFD50SaF9WXpZabquE2wvK3jYsSzZj6Vr6FgWypZA9mRk-dgEEic42bq97SfsN-6X7MhWLoUxKHszsiSMzkWf5HO-A_A6cDki6sgJEj-hA4rijvKkcDCivSJLAj8Js6rYhOz3w9EourOxOSYXpuaHWF-4Gcuo_LUxcHMhvWXl-H3x461HKhQ-gSYnPQoa0Hz3qTvsrV2xJ6vqcnTOEA6XHdfyk5pQns3ohzvSBmZug9Vqt-m2_vM7D-CphZnsqtaLQ9jB4ghaqxIOzFr0MZQDkiX-_vkrNSz_NUMHS6sUSTaekq9hC8ynNj-pYKpImTZ42wQY1U0mcD6nIThntgJFzszlLlvWeSc0w70qp0xN8llJ7dNnMOzeDK5vHVuGwdFk_eQODeVahuSNPO6hwCzzEtGRMvTdRPtai1SQoCUqj6AUpjz0s9SVWSi4q0WQcf8EGsWswFNgaAjUFFd-gAlHISMV-FpwT-sojAjsteHNShSxthzlplTGJF6dVcxixtVituHVuu-8Zub4a6_zlURja52L2PxaNDhN0OvLSnb_mCG-GX3-Uj2dPabzS9i7HXzsxb33_Q_PYb9DWKiOMDuHxrL8ii9gV39bjhflhVXaP2z-9Cw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA7eEF-8i3cD-qJQadc0aR_FbSjKEC8wn0qanozB1o1u3t78Cf5Gf4knbTYVRBDfSnoSSk7OyZf0nO8QchC4DABU5ASJn-ABRTJHeoI7EOFeoZPAT0JdFJsQjUbYbEZXNjbH5MKU_BDjCzdjGYW_NgYO_VR_sXJ4Hrwce7iEwkkyzYKIo11OV6_rd5djV-yJorocnjO4w0TFtfykJpTns_f3HekTZn4Fq8VuU1_453cuknkLM-lJuS6WyARky2RhVMKBWoteIfkt6hLeX99Sw_JfMnTQtEiRpO0u-ho6gFbX5idlVGYpVQZvmwCjsskEzrewC_SprUDRouZylw7LvBMc4UnmXSo7rV6O7d1Vclev3Z6eObYMg6PQ-tEdGso1DeiNPOYBB629hFeECH03Ub5SPOWoaAHSQygFKQt9nbpCh5y5igea-WtkKutlsE4oGAI1yaQfQMKAi0gGvuLMUyoKIwR7G-RwpIpYWY5yUyqjE4_OKmYy42IyN8j-WLZfMnP8KLU90mhsrXMQm1-LBqdxfH1U6O6XEeJa8-a-eNr8i_Aemb2q1uPL88bFFpmrIBQqA8y2ydQwf4AdMqMeh-1BvmvX7AcLofOn
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Three%E2%80%90dimensional+dental+image+segmentation+and+classification+using+deep+learning+with+tunicate+swarm+algorithm&rft.jtitle=Expert+systems&rft.au=Awari%2C+Harshavardhan&rft.au=Subramani%2C+Neelakandan&rft.au=Janagaraj%2C+Avanija&rft.au=Balasubramaniapillai+Thanammal%2C+Geetha&rft.date=2024-06-01&rft.issn=0266-4720&rft.eissn=1468-0394&rft.volume=41&rft.issue=6&rft.epage=n%2Fa&rft_id=info:doi/10.1111%2Fexsy.13198&rft.externalDBID=10.1111%252Fexsy.13198&rft.externalDocID=EXSY13198
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0266-4720&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0266-4720&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0266-4720&client=summon