Performance of Convolutional Neural Networks and Transfer Learning for Skeletal Bone Age Assessment

Main Article Content

Chadaporn Keatmanee, PhD
Songphon Klabwong, MSc
Kamolphong Osatavanichvong, MD
Chirotchana Suchato, MD

Abstract

OBJECTIVES: Bone age assessment is used by clinicians for estimating the maturity of a child’s skeletal system. Traditionally, physicians use template matching methods (GP and/or TW2). Time and accuracy of the evaluation rely on a physician’s experience. Therefore, this research proposes a fully automatic system for bone age assessment with cutting edge artificial Intelligence (AI) technology. 
 
MATERIAL AND METHODS: Convolutional Neural Network (CNNs), a Deep Learning (DL) technique is applied to skeletal bone age prediction combined with transfer learning algorithm. Hence, various kinds of transfer learning algorithms (ResNet-50, Inception-V3, and VGG-16) are investigated in training in the proposed model fed by a number of x-ray images (12,000 image approximately—imbalanced data). 

RESULT: VGG-16 shows significant accuracy compared to ResNet-50 and Inception-V3 (mae = 6.53, 20.52 and 43.11 months respectively)


CONCLUSION: The most effective pre-trained layer for CNNs in bone age assessment is VGG-16 according to the accuracy of its prediction. 



 

Article Details

How to Cite
1.
Keatmanee C, Klabwong S, Osatavanichvong K, Suchato C. Performance of Convolutional Neural Networks and Transfer Learning for Skeletal Bone Age Assessment. BKK Med J [Internet]. 2019 Jun. 26 [cited 2024 Mar. 29];15(1):1. Available from: https://he02.tci-thaijo.org/index.php/bkkmedj/article/view/197910
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Original Article

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