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Proceedings Paper

Neural net computing for biomedical image processing
Author(s): Anke Meyer-Baese
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Paper Abstract

In this paper we describe some of the most important types of neural networks applied in biomedical image processing. The networks described are variations of well-known architectures but are including image-relevant features in their structure. Convolutional neural networks, modified Hopfield networks, regularization networks and nonlinear principal component analysis neural networks are successfully applied in biomedical image classification, restoration and compression.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342897
Show Author Affiliations
Anke Meyer-Baese, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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