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A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment

N. Shobha Rani1, *, M. Chandrajith2, B. R. Pushpa1, B. J. Bipin Nair1

1 Department of Computer Science, Amrita School of Arts and Sciences, Mysuru, Amrita Vishwa Vidyapeetham, India.
2 Maharaja Institute of Technology, Mysuru, India.

* Corresponding Author: N. Shobha Rani. Email: email.

Computers, Materials & Continua 2020, 62(2), 679-694. https://doi.org/10.32604/cmc.2020.08552

Abstract

Assessing the age of an individual via bones serves as a fool proof method in true determination of individual skills. Several attempts are reported in the past for assessment of chronological age of an individual based on variety of discriminative features found in wrist radiograph images. The permutation and combination of these features realized satisfactory accuracies for a set of limited groups. In this paper, assessment of gender for individuals of chronological age between 1-17 years is performed using left hand wrist radiograph images. A fully automated approach is proposed for removal of noise persisted due to non-uniform illumination during the process of radiograph acquisition process. Subsequent to this a computational technique for extraction of wrist region is proposed using operations on specific bit planes of image. A framework called GeNet of deep convolutional neural network is applied for classification of extracted wrist regions into male and female. The experimentations are conducted on the datasets of Radiological Society of North America (RSNA) of about 12442 images. Efficiency of preprocessing and segmentation techniques resulted into a correlation of about 99.09%. Performance of GeNet is evaluated on the extracted wrist regions resulting into an accuracy of 82.18%.

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APA Style
Rani, N.S., Chandrajith, M., Pushpa, B.R., Nair, B.J.B. (2020). A deep convolutional architectural framework for radiograph image processing at bit plane level for gender & age assessment. Computers, Materials & Continua, 62(2), 679-694. https://doi.org/10.32604/cmc.2020.08552
Vancouver Style
Rani NS, Chandrajith M, Pushpa BR, Nair BJB. A deep convolutional architectural framework for radiograph image processing at bit plane level for gender & age assessment. Comput Mater Contin. 2020;62(2):679-694 https://doi.org/10.32604/cmc.2020.08552
IEEE Style
N.S. Rani, M. Chandrajith, B.R. Pushpa, and B.J.B. Nair "A Deep Convolutional Architectural Framework for Radiograph Image Processing at Bit Plane Level for Gender & Age Assessment," Comput. Mater. Contin., vol. 62, no. 2, pp. 679-694. 2020. https://doi.org/10.32604/cmc.2020.08552

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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