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http://dx.doi.org/10.6109/jkiice.2018.22.10.1314

Convolution Neural Network based TW3 Maximum Height Prediction System  

Park, Si-hyeon (Research&evelopment Center)
Cho, Young-bok (Department of Computer & Information Security, Daejeon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.10, 2018 , pp. 1314-1319 More about this Journal
Abstract
The current TW3 - based maximum height prediction technique used in KMAA(Korean Medical Academy of Auxology) is manual and subjective, and it requires a lot of time and effort in the medical treatment, while the interest in the child's growth is very high. In addition, the technique of classifying images using deep learning, especially convolutional neural networks, is used in many fields at a more accurate level than the human eyes, also there is no exception in the medical field. In this paper, we introduce a TW3 algorithm using deep learning, that uses the convolutional neural network to predict the growth level of the left hand bone, to predict the maximum height of child and youth in order to increase the reliability of predictions and improve the convenience of the doctor.
Keywords
CNN; TW3; Bone Age; Maximum Height Prediction; Hand Bone;
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Times Cited By KSCI : 6  (Citation Analysis)
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