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Hand Bone Age Estimation Using Deep Convolutional Neural Networks

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13087))

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Abstract

Bone age is an indicator of skeletal maturity and is widely used by doctors to diagnose patients with growth disorders. Bone age estimation is predominantly determined by a human rater comparing an X-ray image with a set of standards plates (GP) or a set of stages (TW). In both methods, the process is tedious and highly error-prone. This paper presents a novel approach that uses a U-net and a deep convolutional neural network to predict bone age from a hand radiograph. First, background objects are discarded from an X-ray, which is the combined with sex information to predict a bone age in months. The proposed model achieves a mean absolute error of 8.04 months when tested on 1259 X-rays of both sexes, comparable to state-of-the-art performance.

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Author information

Authors and Affiliations

  1. Discipline of Electrical, Electronic and Computer Engineering, University of KwaZulu-Natal, Durban, 4041, South Africa

    Antoine Badi Mame & Jules R. Tapamo

Authors
  1. Antoine Badi Mame
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  2. Jules R. Tapamo
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Corresponding author

Correspondence to Jules R. Tapamo .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

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Mame, A.B., Tapamo, J.R. (2022). Hand Bone Age Estimation Using Deep Convolutional Neural Networks. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-95405-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95404-8

  • Online ISBN: 978-3-030-95405-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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