Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets

Wojciech M. Grohman; Atam P. Dhawan

Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets

Wojciech M. Grohman, Atam P. Dhawan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A novel convex set based neuro-fuzzy algorithm for classification of difficult-to-diagnose instances of breast cancer is described in this paper. The new approach offers rational advantages over the leading neural algorithm - backpropagation. The comparative results obtained using receiver operating characteristic (ROC) analysis show that the ability of the convex set based method to infer knowledge is better than that of backpropagation, making it more suitable for use in real diagnostic systems.

Original language	English (US)
Title of host publication	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Publisher	IEEE
Pages	1132
Number of pages	1
ISBN (Print)	0780356756
State	Published - 1999
Externally published	Yes
Event	Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) - Atlanta, GA, USA Duration: Oct 13 1999 → Oct 16 1999

Publication series

Name	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
ISSN (Print)	0589-1019

Other

Other	Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)
City	Atlanta, GA, USA
Period	10/13/99 → 10/16/99

All Science Journal Classification (ASJC) codes

Signal Processing
Health Informatics
Computer Vision and Pattern Recognition
Biomedical Engineering

Cite this

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title = "Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets",

abstract = "A novel convex set based neuro-fuzzy algorithm for classification of difficult-to-diagnose instances of breast cancer is described in this paper. The new approach offers rational advantages over the leading neural algorithm - backpropagation. The comparative results obtained using receiver operating characteristic (ROC) analysis show that the ability of the convex set based method to infer knowledge is better than that of backpropagation, making it more suitable for use in real diagnostic systems.",

author = "Grohman, {Wojciech M.} and Dhawan, {Atam P.}",

year = "1999",

language = "English (US)",

isbn = "0780356756",

series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

publisher = "IEEE",

pages = "1132",

booktitle = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

note = "Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS) ; Conference date: 13-10-1999 Through 16-10-1999",

}

Grohman, WM & Dhawan, AP 1999, Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets. in Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 2, IEEE, pp. 1132, Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS), Atlanta, GA, USA, 10/13/99.

Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets. / Grohman, Wojciech M.; Dhawan, Atam P.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. IEEE, 1999. p. 1132 (Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets

AU - Grohman, Wojciech M.

AU - Dhawan, Atam P.

PY - 1999

Y1 - 1999

N2 - A novel convex set based neuro-fuzzy algorithm for classification of difficult-to-diagnose instances of breast cancer is described in this paper. The new approach offers rational advantages over the leading neural algorithm - backpropagation. The comparative results obtained using receiver operating characteristic (ROC) analysis show that the ability of the convex set based method to infer knowledge is better than that of backpropagation, making it more suitable for use in real diagnostic systems.

AB - A novel convex set based neuro-fuzzy algorithm for classification of difficult-to-diagnose instances of breast cancer is described in this paper. The new approach offers rational advantages over the leading neural algorithm - backpropagation. The comparative results obtained using receiver operating characteristic (ROC) analysis show that the ability of the convex set based method to infer knowledge is better than that of backpropagation, making it more suitable for use in real diagnostic systems.

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BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

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T2 - Proceedings of the 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Fall Meeting of the Biomedical Engineering Society (1st Joint BMES / EMBS)

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ER -

Classification of difficult-to-diagnose microcalcifications using fuzzy neural network with convex sets

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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