  Estimating the Bone Age of children is very important for diagnosing growth
defects, and related diseases, and estimating the final height that children
reach after maturity. For this reason, it is widely used in different
countries. Traditional methods for estimating bone age are performed by
comparing atlas images and radiographic images of the left hand, which is
time-consuming and error-prone. To estimate bone age using deep neural network
models, a lot of research has been done, our effort has been to improve the
accuracy and speed of this process by using the introduced approach. After
creating and analyzing our initial model, we focused on preprocessing and made
the inputs smaller, and increased their quality. we selected small regions of
hand radiographs and estimated the age of the bone only according to these
regions. by doing this we improved bone age estimation accuracy even further
than what was achieved in related works, without increasing the required
computational resource. We reached a Mean Absolute Error (MAE) of 3.90 months
in the range of 0-20 years and an MAE of 3.84 months in the range of 1-18 years
on the RSNA test set.
