A new two-feature GBAM-neurodynamical classifier for breast cancer diagnosis

Tijana Ivancevic; Lakhmi Jain; Murk Bottema

doi:10.1109/KES.1999.820182

A new two-feature GBAM-neurodynamical classifier for breast cancer diagnosis

Tijana Ivancevic, Lakhmi Jain, Murk Bottema

College of Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

8 Citations (Scopus)

Abstract

Like standard discrete artificial neural networks (ANNs), continual neurodynamical systems can be used for classification and diagnosis of breast cancer. In this paper a two-feature generalized bidirectional associative memory classifier is formulated in tensorial invariant form. It is implemented in `Mathematica 3.0' and tested on two sample features (radius and perimeter of cell nuclei in free needle aspiration slides) from Wisconsin breast-cancer database. The classification accuracy obtained (86%) together with invariance of the classification result upon the variation of dimension and output-form of neural activation fields, shows the potential classification ability of theoretical classifiers directly-implemented into computer algebra systems.

Original language	English
Title of host publication	1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems
Editors	L. C. Jain
Place of Publication	Piscataway, NJ, USA
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	296-299
Number of pages	4
ISBN (Print)	0-7803-5578-4
DOIs	https://doi.org/10.1109/KES.1999.820182
Publication status	Published - 1999
Event	Proceedings of the 1999 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES '99) - Adelaide, Aust Duration: 31 Aug 1999 → 1 Sep 1999

Conference

Conference	Proceedings of the 1999 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES '99)
City	Adelaide, Aust
Period	31/08/99 → 1/09/99

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abstract = "Like standard discrete artificial neural networks (ANNs), continual neurodynamical systems can be used for classification and diagnosis of breast cancer. In this paper a two-feature generalized bidirectional associative memory classifier is formulated in tensorial invariant form. It is implemented in `Mathematica 3.0' and tested on two sample features (radius and perimeter of cell nuclei in free needle aspiration slides) from Wisconsin breast-cancer database. The classification accuracy obtained (86%) together with invariance of the classification result upon the variation of dimension and output-form of neural activation fields, shows the potential classification ability of theoretical classifiers directly-implemented into computer algebra systems.",

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Ivancevic, T, Jain, L & Bottema, M 1999, A new two-feature GBAM-neurodynamical classifier for breast cancer diagnosis. in LC Jain (ed.), 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ, USA, pp. 296-299, Proceedings of the 1999 3rd International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES '99), Adelaide, Aust, 31/08/99. https://doi.org/10.1109/KES.1999.820182

A new two-feature GBAM-neurodynamical classifier for breast cancer diagnosis. / Ivancevic, Tijana; Jain, Lakhmi; Bottema, Murk.

1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. ed. / L. C. Jain. Piscataway, NJ, USA : Institute of Electrical and Electronics Engineers Inc., 1999. p. 296-299.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Jain, Lakhmi

AU - Bottema, Murk

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N2 - Like standard discrete artificial neural networks (ANNs), continual neurodynamical systems can be used for classification and diagnosis of breast cancer. In this paper a two-feature generalized bidirectional associative memory classifier is formulated in tensorial invariant form. It is implemented in `Mathematica 3.0' and tested on two sample features (radius and perimeter of cell nuclei in free needle aspiration slides) from Wisconsin breast-cancer database. The classification accuracy obtained (86%) together with invariance of the classification result upon the variation of dimension and output-form of neural activation fields, shows the potential classification ability of theoretical classifiers directly-implemented into computer algebra systems.

AB - Like standard discrete artificial neural networks (ANNs), continual neurodynamical systems can be used for classification and diagnosis of breast cancer. In this paper a two-feature generalized bidirectional associative memory classifier is formulated in tensorial invariant form. It is implemented in `Mathematica 3.0' and tested on two sample features (radius and perimeter of cell nuclei in free needle aspiration slides) from Wisconsin breast-cancer database. The classification accuracy obtained (86%) together with invariance of the classification result upon the variation of dimension and output-form of neural activation fields, shows the potential classification ability of theoretical classifiers directly-implemented into computer algebra systems.

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Y2 - 31 August 1999 through 1 September 1999

ER -

A new two-feature GBAM-neurodynamical classifier for breast cancer diagnosis

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