Classification of hand‐wrist maturity level based on similarity matching

Abstract Judging the maturity level of each hand‐wrist reference bone is the core issue in bone age assessment. Relying on the superiority of convolutional neural networks in feature representation, deep learning is widely studied for the automatic bone age assessment. However, an efficient but complex deep learning network requests a large dataset with bone‐maturity‐level labels for training, restricting its large‐scale application in bone maturity classification. For this reason, we transform the bone‐maturity‐level classification problem into the similarity matching problem. Also, we propose a general structure based on Siamese network by merging two inputs into a two‐channel input and introducing a dual attention mechanism, to create an Attentional Two‐Channel Network (ATC‐Net). This paper takes the intermediate phalanges III as an example to assess the performance of the similarity matching method and the ATC‐Net. Experiments show that our method can perform better on small datasets, which effectively makes up for the data shortage problem. The ATC‐Net used for classification significantly reduces the evaluation time compared with other classical networks. It reduces the time of assessing one sample by about 49% as compared to VGG‐16. And more importantly, it achieves the highest classification accuracy of 92.74% among all investigated networks.