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 language||American English|
|Title of host publication||Intelligent Computing - Proceedings of the 2019 Computing Conference|
|Editors||Kohei Arai, Rahul Bhatia, Supriya Kapoor|
|Number of pages||11|
|State||Published - 2019|
|Event||Computing Conference, 2019 - London, United Kingdom|
Duration: 16 Jul 2019 → 17 Jul 2019
|Name||Advances in Intelligent Systems and Computing|
|Conference||Computing Conference, 2019|
|Period||16/07/19 → 17/07/19|
Bibliographical noteFunding Information:
This research was supported by the Israel Science Foundation and by the Israel Ministry of Science and Technology.
© 2019, Springer Nature Switzerland AG.
- Deep learning network
- Machine vision
- Medical diagnostic
- Neural network
- Object detection