Hand bone age estimation using divide and conquer strategy and lightweight convolutional neural networks

TL;DR

This paper introduces a divide and conquer approach with lightweight CNNs for hand bone age estimation, improving accuracy and efficiency by focusing on small hand regions, achieving a MAE of around 3.9 months.

Contribution

The study proposes a novel divide and conquer strategy with optimized preprocessing and regional analysis, enhancing accuracy without increasing computational costs.

Findings

Achieved MAE of 3.90 months on RSNA dataset

Improved accuracy over previous methods

Maintained low computational resource requirements

Abstract

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.