Abstract
In this paper, we proposed an age assessment method evaluated on Malaysian children aged between 1 and 17. The approach is based on global fuzzy segmentation, local feature extraction using a projection-based feature transform and a designed deep convolutional neural networks (DCNNs) model. In the first step, a global labelling process was achieved based on fuzzy segmentation, and then, the first-to-third molar teeth were segmented. The deformation invariant features were next extracted based on an intensity projection technique. This technique provided high-order features which were invariant to rotation and partial deformation changes. Finally, the designed DCNN model extracts a large set of features in the hierarchical layers which provided scale, rotation and deformation invariance. The method using this approach was evaluated using a comprehensive and labelled orthopantomographs of 456 patients, which were captured in the Department of Dentistry and Research at Universiti Sains Islam Malaysia. The results from the analysis have suggested that the method can classify the images with high performance, which enabled automated age estimation with high accuracy.
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Acknowledgements
We appreciate the support received from the Faculty of Dentistry at the Universiti Sains Islam Malaysia for the X-ray images, the laboratory facilities and their funding support. We would also like to thank the Centre for Artificial Intelligence Technology at the National University of Malaysia for research funds.
Funding
This work was supported by the Universiti Sains Islam Malaysia with the research funds under Grant No. PPP/UTG-0123/FST/30/12213 and the National University of Malaysia DIP-2016-018.
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S.M.M.K., N.S.A. and W.I. were involved in conceptualisation and validation; S.M.M.K. performed methodology and software; W.I. and N.S.A. conducted formal analysis and investigation; N.S.A. analysed data curation and resources; S.M.M.K. prepared original draft; S.M.M.K. and M.A. reviewed and edited the manuscript; S.M.M.K. and M.J.N. performed visualisation; M.J.N. and W.I. were involved in supervision and project administration; and W.I. was involved in funding acquisition
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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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Kahaki, S.M.M., Nordin, M.J., Ahmad, N.S. et al. Deep convolutional neural network designed for age assessment based on orthopantomography data. Neural Comput & Applic 32, 9357–9368 (2020). https://doi.org/10.1007/s00521-019-04449-6
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DOI: https://doi.org/10.1007/s00521-019-04449-6