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Proceedings Paper

Bone age detection via carpogram analysis using convolutional neural networks
Author(s): Felipe Torres; María Alejandra Bravo; Emmanuel Salinas; Gustavo Triana; Pablo Arbeláez
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Paper Abstract

Bone age assessment is a critical factor for determining delayed development in children, which can be a sign of pathologies such as endocrine diseases, growth abnormalities, chromosomal, neurological and congenital disorders among others. In this paper we present BoneNet, a methodology to assess automatically the skeletal maturity state in pediatric patients based on Convolutional Neural Networks. We train and evaluate our algorithm on a database of X-Ray images provided by the hospital Fundacion Santa Fe de Bogot ´ a with around 1500 images of patients between the ages 1 to 18. ´ We compare two different architectures to classify the given data in order to explore the generality of our method. To accomplish this, we define multiple binary age assessment problems, dividing the data by bone age and differentiating the patients by their gender. Thus, exploring several parameters, we develop BoneNet. Our approach is holistic, efficient, and modular, since it is possible for the specialists to use all the networks combined to determine how is the skeletal maturity of a patient. BoneNet achieves over 90% accuracy for most of the critical age thresholds, when differentiating the images between over or under a given age.

Paper Details

Date Published: 17 November 2017
PDF: 8 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 1057217 (17 November 2017); doi: 10.1117/12.2285949
Show Author Affiliations
Felipe Torres, Univ. de los Andes (Colombia)
María Alejandra Bravo, Univ. de los Andes (Colombia)
Emmanuel Salinas, Fundación Sante Fe de Bogotá (Colombia)
Gustavo Triana, Fundación Sante Fe de Bogotá (Colombia)
Pablo Arbeláez, Univ. de los Andes (Colombia)

Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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