Publication:
Multi-level Multi-scale Deep Feature Encoding for Chronological Age Estimation from OPG Images

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dc.contributor.authorAlkaabi S.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorid57212311690en_US
dc.contributor.authorid16023225600en_US
dc.date.accessioned2023-05-29T09:36:01Z
dc.date.available2023-05-29T09:36:01Z
dc.date.issued2022
dc.description.abstractAge estimation is a complex task in forensic dentistry especially if the bodies have started to decompose. However, when the task involves Manually examining, the accuracy can decrease due varying experience of the experts, the results of different experts may also vary. To improve speed and accuracy of the age estimation process using forensic dentistry, researchers have proposed Convolutional Neural Network for Dental Age and Sex Network estimation (DASNET). However, pooling and scalar outputs of CNNs could not allow to get the equivariance due to the dental extraction complexity from panoramic images including jaws, teeth, lesions and carries. So, a deep auto-encoder decoder architecture has been developed by the authors, which estimates the age based on semantic and structural feature representation. The age ranges are chosen based on the structural variation of the jaw in these particular age ranges as compared to each other. The authors have proposed a Convolution Long Short Term Memory (ConvLSTM) to capture the correlation of features and generates high level representation of features. For the representation of the generated features, authors have utilized �Atrous pyramid convolution� to produce a multiscale representation. The authors have proposed a combination of multi-scale and multi-level architecture for age estimation. First comes the first sub-part of the model that is the multi-level architecture, it is used for the extraction of hidden features. After that, the output is fed to second subpart which is the multi-scale architecture that enriches the model representation capability in encoding structural and shape characteristics. The propose techniques successfully reduces mean error to 0.75 years, as opposed to 0.93 years of the DASNET. � 2022 Journal of Image and Graphics.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18178/joig.10.4.151-157
dc.identifier.epage157
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85140446367
dc.identifier.spage151
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85140446367&doi=10.18178%2fjoig.10.4.151-157&partnerID=40&md5=a89ebf785238ca3766aaa044d189563f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26645
dc.identifier.volume10
dc.publisherUniversity of Portsmouthen_US
dc.relation.ispartofAll Open Access, Hybrid Gold
dc.sourceScopus
dc.sourcetitleJournal of Image and Graphics(United Kingdom)
dc.titleMulti-level Multi-scale Deep Feature Encoding for Chronological Age Estimation from OPG Imagesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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