A Novel Approach for Bone Age Assessment using Deep Learning

Authors

  • Nishan B. Poojary  Student, Department of Electronics and Telecommunication Engineering, K.J. Somaiya Institute of Engineering and Information Technology, University of Mumbai, Mumbai, Maharashtra, India
  • Prathamesh G. Pokhare  Student, Department of Electronics and Telecommunication Engineering, K.J. Somaiya Institute of Engineering and Information Technology, University of Mumbai, Mumbai, Maharashtra, India
  • Pratik P. Poojary  Student, Department of Electronics and Telecommunication Engineering, K.J. Somaiya Institute of Engineering and Information Technology, University of Mumbai, Mumbai, Maharashtra, India
  • Charmi D. Raghavani  Student, Department of Electronics and Telecommunication Engineering, K.J. Somaiya Institute of Engineering and Information Technology, University of Mumbai, Mumbai, Maharashtra, India
  • Dr. Jayashree Khanapuri  Professor, Department of Electronics and Telecommunication Engineering, K.J. Somaiya Institute of Engineering and Information Technology, University of Mumbai, Mumbai, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT21731

Keywords:

Bone Age Prediction, Convolutional Neural Network (CNN), Deep Learning, Transfer Learning, Image Processing, Histogram Equalization, Mean Average Error (MAE)

Abstract

In this paper, we propose a detailed approach to create a Bone age assessment model. Bone age assessment is a common medical practice in the assessment of child development, who are less than 18 years of age. In this proposed model, the Xception architecture is being used for transfer learning. Using feature extraction and transfer learning, the pre-trained convolutional neural network were custom trained. The dataset used for training the model is obtained from the Kaggle RNSA Bone Age dataset containing 12811 male and female bone images of different age groups. Finally, we were able to attain a mean absolute error (MAE) of 8.175 months in male and female patients, which aligns with our initial goal of achieving MAE in under a year.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

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How to Cite

[1]
Nishan B. Poojary, Prathamesh G. Pokhare, Pratik P. Poojary, Charmi D. Raghavani, Dr. Jayashree Khanapuri, " A Novel Approach for Bone Age Assessment using Deep Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.67-75, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT21731