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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

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 679-694, 2020, DOI: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… More >

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