Deep Learning for Fracture Detection in the Radius and Ulna on Conventional Radiographs

J. Verboom

Master thesis 2021.

This work gives a compartmentalized overview of a fracture detection tool to detect and localize fractures in the radius and ulna on conventional radiographs using deep learning.

This contrasts earlier studies in that it proposes a more efficient object detector, demonstrates the generalizability of fracture detection models to data from a different hospital, and employs more advanced class activation mapping methods for fracture localization.

Both RadboudUMC and the Jeroen Bosch Ziekenhuis provided data to create a multi-institutional dataset.

The two data sources enabled me to demonstrate how fracture detection classifiers trained on data from only one institution significantly perform less when tested on data from another institution.

Moreover, this study demonstrates a more efficient bone localization method that yields adequate performance to be used for cropping regions of interest, and a newer fracture localization method (ScoreCAM) that outperforms its predecessors in terms of highlighting less redundant information.

I conclude that the algorithms presented in this work show the potential to be incorporated in a clinically usable fracture detection tool.

However, more research needs to be conducted using multi-institutional for training fracture detection classifiers.