Human Bone Age Estimation of Carpal Bone X-Ray Using Residual Network with Batch Normalization Classification

Anisah Nabilah - Politeknik Elektronika Negeri Surabaya, Surabaya ,60111, Indonesia
Riyanto Sigit - Politeknik Elektronika Negeri Surabaya, Surabaya ,60111, Indonesia
Arna Fariza - Politeknik Elektronika Negeri Surabaya, Surabaya ,60111, Indonesia
Madyono Madyono - Politeknik Elektronika Negeri Surabaya, Surabaya ,60111, Indonesia

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Bone age is an index used by pediatric radiology and endocrinology departments worldwide to define skeletal maturity for medical and non-medical purposes. In general, the clinical method for bone age assessment (BAA) is based on examining the visual ossification of individual bones in the left hand and then comparing it with a standard radiographic atlas of the hand. However, this method is highly dependent on the experience and conditions of the forensic expert. This paper proposes a new approach to age estimation of human bone based on the carpal bones in the hand and using a residual network architecture. The classification layer was modified with batch normalization to optimize the training process. Before carrying out the training process, we performed an image augmentation technique to make the dataset more varied. The following augmentation techniques were used: resizing; random affine transformation; horizontal flipping; adjusting brightness, contrast, saturation, and hue; and image inversion. The output is the classification of bone age in the range of 1 to 19 years. The results obtained when using a VGG16 model were an MAE value of 5.19 and an R2 value of 0.56 while using the newly developed ResNeXt50(32x4d) model produced an MAE value of 4.75 and an R2 value of 0.63. The research results indicate that the proposed modification of the residual training model improved classification compared to using the VGG16 model, as indicated by an MAE value of 4.75 and an R2 value of 0.63.


Forensics; carpal bone; convolution neural network; bone age; batch normalization.

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