Adaptive bone abnormality detection in medical imagery using deep neural networks. / Storey, Oliver; Wei, Bo; Zhang, Li; Romuald Fotso Mtope, Franck.

World Scientific Proceedings Series on Computer Engineering and Information Science. In: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12. : World Scientific, Singapore, 2020. p. 915-922 .

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published
  • Oliver Storey
  • Bo Wei
  • Li Zhang
  • Franck Romuald Fotso Mtope

Abstract

This research conducts transfer learning with optimal training option identification for the detection of wrist bone abnormalities in X-Ray imagery. Specifically, transfer learning based on Convolutional Neural Networks (CNNs), such as ResNet-18 and GoogLeNet, has been developed for wrist bone abnormality detection. The effect of altering the number of epochs on the network performance using an automatic process is also investigated. The MURA wrist radiological images are extracted in our experiments. The proposed system achieves a superior performance for wrist bone abnormality detection in comparison with those of existing studies.
Original languageEnglish
Title of host publicationWorld Scientific Proceedings Series on Computer Engineering and Information Science
Place of PublicationIn: Developments of Artificial Intelligence Technologies in Computation and Robotics. World Scientific Proceedings Series on Computer Engineering and Information Science, 12.
PublisherWorld Scientific, Singapore
Pages915-922
Number of pages8
DOIs
Publication statusPublished - 15 Aug 2020
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43383728