Neural network diagnosis of avascular necrosis from magnetic resonance images

Armando Manduca; Paul S. Christy; Richard L. Ehman

doi:10.1117/12.154547

14 September 1993 Neural network diagnosis of avascular necrosis from magnetic resonance images

Armando Manduca, Paul S. Christy, Richard L. Ehman

Proceedings Volume 1898, Medical Imaging 1993: Image Processing; (1993) https://doi.org/10.1117/12.154547
Event: Medical Imaging 1993, 1993, Newport Beach, CA, United States

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 X 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.

Citation Download Citation

Armando Manduca, Paul S. Christy, and Richard L. Ehman "Neural network diagnosis of avascular necrosis from magnetic resonance images", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154547

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available