  Bone age assessment is an important clinical trial to measure skeletal child
maturity and diagnose of growth disorders. Conventional approaches such as the
Tanner-Whitehouse (TW) and Greulich and Pyle (GP) may not perform well due to
their large inter-observer and intra-observer variations. In this paper, we
propose a finger joint localization strategy to filter out most non-informative
parts of images. When combining with the conventional full image-based deep
network, we observe a much-improved performance. % Our approach utilizes full
hand and specific joints images for skeletal maturity prediction. In this
study, we applied powerful deep neural network and explored a process in the
forecast of skeletal bone age with the specifically combine joints images to
increase the performance accuracy compared with the whole hand images.
