Neural network diagnosis of avascular necrosis from magnetic resonance images

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

2 Scopus citations

Abstract

We have explored the use of artificial neural networks to diagnose avascular necrosis (AVN) of the femoral head from magnetic resonance images. We have developed multi-layer 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. 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 are described.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsMurray H. Loew
PublisherPubl by Society of Photo-Optical Instrumentation Engineers
Pages598-603
Number of pages6
ISBN (Print)0819411310
StatePublished - Dec 1 1993
EventMedical Imaging 1993: Image Processing - Newport Beach, CA, USA
Duration: Feb 14 1992Feb 19 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1898
ISSN (Print)0277-786X

Other

OtherMedical Imaging 1993: Image Processing
CityNewport Beach, CA, USA
Period2/14/922/19/92

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ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Manduca, A., Christy, P. S., & Ehman, R. L. (1993). Neural network diagnosis of avascular necrosis from magnetic resonance images. In M. H. Loew (Ed.), Proceedings of SPIE - The International Society for Optical Engineering (pp. 598-603). (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 1898). Publ by Society of Photo-Optical Instrumentation Engineers.