RESEARCH ARTICLE
Identifying Skeletal Maturity from X-rays using Deep Neural Networks
Suprava Patnaik1, *, Sourodip Ghosh1, Richik Ghosh1, Shreya Sahay1
Article Information
Identifiers and Pagination:
Year: 2021Volume: 15
Issue: Suppl-2, M3
First Page: 141
Last Page: 148
Publisher ID: TOBEJ-15-141
DOI: 10.2174/1874120702115010141
Article History:
Received Date: 26/7/2020Revision Received Date: 11/1/2021
Acceptance Date: 13/1/2021
Electronic publication date: 31/12/2021
Collection year: 2021
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.
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.