Neural network diagnosis of avascular necrosis from magnetic resonance images

A. Manduca, P. Christy, R. Ehman

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

2 Scopus citations

Abstract

Artificial neural networks has been used to diagnose avascular necrosis (AVN) of the femoral head from magnetic resonance images. Multilayer perceptron networks, trained with conjugate gradient optimization, which diagnose AVN from single sagittal images of the femoral head with 100% accuracy on the training data and 97% accuracy on test data has been developed. These networks use only the raw image as input (with minimal preprocessing to average the images down to 32 × 32 size and to scale the input data values) and learn to extract their own features for the diagnosis decision. Various experiments with these networks, whose results are considered to be very encouraging for the use of neural networks in diagnostic radiology, are described.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual Conference on Engineering in Medicine and Biology
PublisherPubl by IEEE
Pages1429-1431
Number of pages3
Editionpt 3
ISBN (Print)0780302168
StatePublished - Dec 1 1991
EventProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Orlando, FL, USA
Duration: Oct 31 1991Nov 3 1991

Publication series

NameProceedings of the Annual Conference on Engineering in Medicine and Biology
Numberpt 3
Volume13
ISSN (Print)0589-1019

Other

OtherProceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
CityOrlando, FL, USA
Period10/31/9111/3/91

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Manduca, A., Christy, P., & Ehman, R. (1991). Neural network diagnosis of avascular necrosis from magnetic resonance images. In Proceedings of the Annual Conference on Engineering in Medicine and Biology (pt 3 ed., pp. 1429-1431). (Proceedings of the Annual Conference on Engineering in Medicine and Biology; Vol. 13, No. pt 3). Publ by IEEE.