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

Erez Yahalomi; Michael Chernofsky; Michael Werman

doi:10.1007/978-3-030-22871-2_69

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

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

40 Scopus citations

Abstract

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	English
Title of host publication	Intelligent Computing - Proceedings of the 2019 Computing Conference
Editors	Kohei Arai, Rahul Bhatia, Supriya Kapoor
Publisher	Springer Verlag
Pages	971-981
Number of pages	11
ISBN (Print)	9783030228705
DOIs	https://doi.org/10.1007/978-3-030-22871-2_69
State	Published - 2019
Event	Computing Conference, 2019 - London, United Kingdom Duration: 16 Jul 2019 → 17 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	997
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Computing Conference, 2019
Country/Territory	United Kingdom
City	London
Period	16/07/19 → 17/07/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Deep learning network
Machine vision
Medical diagnostic
Neural network
Object detection

Access to Document

10.1007/978-3-030-22871-2_69

Cite this

Yahalomi, E., Chernofsky, M., & Werman, M. (2019). Detection of Distal Radius Fractures Trained by a Small Set of X-Ray Images and Faster R-CNN. In K. Arai, R. Bhatia, & S. Kapoor (Eds.), Intelligent Computing - Proceedings of the 2019 Computing Conference (pp. 971-981). (Advances in Intelligent Systems and Computing; Vol. 997). Springer Verlag. https://doi.org/10.1007/978-3-030-22871-2_69

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abstract = "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.",

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Yahalomi, E, Chernofsky, M & Werman, M 2019, Detection of Distal Radius Fractures Trained by a Small Set of X-Ray Images and Faster R-CNN. in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing - Proceedings of the 2019 Computing Conference. Advances in Intelligent Systems and Computing, vol. 997, Springer Verlag, pp. 971-981, Computing Conference, 2019, London, United Kingdom, 16/07/19. https://doi.org/10.1007/978-3-030-22871-2_69

Detection of Distal Radius Fractures Trained by a Small Set of X-Ray Images and Faster R-CNN. / Yahalomi, Erez; Chernofsky, Michael; Werman, Michael.
Intelligent Computing - Proceedings of the 2019 Computing Conference. ed. / Kohei Arai; Rahul Bhatia; Supriya Kapoor. Springer Verlag, 2019. p. 971-981 (Advances in Intelligent Systems and Computing; Vol. 997).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

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

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Keywords

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