RESEARCH ARTICLE


Identifying Skeletal Maturity from X-rays using Deep Neural Networks



Suprava Patnaik1, *, Sourodip Ghosh1, Richik Ghosh1, Shreya Sahay1
1 Department of Electronics Engineering, KIIT University, Bhubaneshwar, India


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Creative Commons License
© 2021 Patnaik. et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at Department of Electronics Engineering, KIIT University, Bhubaneshwar, India; Tel: 7303409222; E-mail: suprava_patnaik@yahoo.com


Abstract

Skeletal maturity estimation is routinely evaluated by pediatrics and radiologists to assess growth and hormonal disorders. Methods integrated with regression techniques are incompatible with low-resolution digital samples and generate bias, when the evaluation protocols are implemented for feature assessment on coarse X-Ray hand images. This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. Further, in the same order, the MAE accomplished by the two models are 12.583 and 13.299 respectively, when subjected to very few training instances with negligible chances of overfitting.

Keywords: Bone age identification, RSNA Bone Age, Deep Neural Networks, Inception-ResNet-V2, Xception Network, Region-of-Interest.