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

Chadaporn Keatmanee, Songphon Klabwong, Kamolphong Osatavanichvong, Chirotchana Suchato


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.

Keywords: deep learning, convolutional neural network, bone age, growth disorder, maturity estimation, transfer learning

DOI: 10.31524/bkkmedj.2019.02.001

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