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Proceedings Paper

Applying neural networks to ultrasonographic texture recognition
Author(s): Jean-Francois Gallant; Jean Meunier; Robert Stampfler; Jocelyn Cloutier
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Paper Abstract

A neural network was trained to classify ultrasound image samples of normal, adenomatous (benign tumor) and carcinomatous (malignant tumor) thyroid gland tissue. The samples themselves, as well as their Fourier spectrum, miscellaneous cooccurrence matrices and 'generalized' cooccurrence matrices, were successively submitted to the network, to determine if it could be trained to identify discriminating features of the texture of the image, and if not, which feature extractor would give the best results. Results indicate that the network could indeed extract some distinctive features from the textures, since it could accomplish a partial classification when trained with the samples themselves. But a significant improvement both in learning speed and performance was observed when it was trained with the generalized cooccurrence matrices of the samples.

Paper Details

Date Published: 2 September 1993
PDF: 11 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152526
Show Author Affiliations
Jean-Francois Gallant, Univ. de Montreal (Canada)
Jean Meunier, Univ. de Montreal (Canada)
Robert Stampfler, Univ. de Montreal (Canada)
Jocelyn Cloutier, Univ. de Montreal (Canada)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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