Detection of Distal Radius Fractures Trained by a Small Set of X-Ray Images and Faster R-CNN

Erez Yahalomi*, Michael Chernofsky, Michael Werman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Scopus citations


Distal radius fractures are the most common fractures of the upper extremity in humans. As such, they account for a significant portion of the injuries that present to emergency rooms and clinics throughout the world. We trained a Faster R-CNN, a machine vision neural network for object detection, to identify and locate distal radius fractures in anteroposterior X-ray images. We achieved an accuracy of 96% in identifying fractures and mean Average Precision, mAP of 0.866. This is significantly more accurate than the detection achieved by physicians and radiologists. These results were obtained by training the deep learning network with only 38 original images of anteroposterior hands X-ray images with fractures. This opens the possibility to detect rare diseases or rare symptoms of common diseases with this type of network, where there is only a small set of diagnosed X-ray images.

Original languageAmerican English
Title of host publicationIntelligent Computing - Proceedings of the 2019 Computing Conference
EditorsKohei Arai, Rahul Bhatia, Supriya Kapoor
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783030228705
StatePublished - 2019
EventComputing Conference, 2019 - London, United Kingdom
Duration: 16 Jul 201917 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceComputing Conference, 2019
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


  • Deep learning network
  • Machine vision
  • Medical diagnostic
  • Neural network
  • Object detection


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