Comparative Analysis of Machine Learning Approaches for Bone Age Assessment: A Comprehensive Study on Three Distinct Models

TL;DR

This study compares three machine learning models—Xception, VGG, and CNN—for automating bone age assessment from X-ray images, aiming to identify the most accurate and efficient approach.

Contribution

It provides a comprehensive comparison of three popular models for bone age prediction, highlighting their relative accuracy and efficiency.

Findings

Xception achieved the lowest MAE among the models

VGG and CNN also showed competitive accuracy levels

The study offers guidance on selecting models based on resource availability

Abstract

Radiologists and doctors make use of X-ray images of the non-dominant hands of children and infants to assess the possibility of genetic conditions and growth abnormalities. This is done by assessing the difference between the actual extent of growth found using the X-rays and the chronological age of the subject. The assessment was done conventionally using The Greulich Pyle (GP) or Tanner Whitehouse (TW) approach. These approaches require a high level of expertise and may often lead to observer bias. Hence, to automate the process of assessing the X-rays, and to increase its accuracy and efficiency, several machine learning models have been developed. These machine-learning models have several differences in their accuracy and efficiencies, leading to an unclear choice for the suitable model depending on their needs and available resources. Methods: In this study, we have analyzed the 3 most widely used models for the automation of bone age prediction, which are the Xception model, VGG model and CNN model. These models were trained on the preprocessed dataset and the accuracy was measured using the MAE in terms of months for each model. Using this, the comparison between the models was done. Results: The 3 models, Xception, VGG, and CNN models have been tested for accuracy and other relevant factors.