Pediatric Bone Age Assessment using Deep Learning Models
This work addresses the need for automated bone age assessment in pediatric radiology, but it is incremental as it applies existing models to a known medical imaging task.
The study tackled pediatric bone age assessment by comparing deep learning models, finding that XceptionNet achieved the lowest mean average error of 0.8 years.
Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age of the input data, and their mean average errors are compared and evaluated to see which model predicts the best.