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

Wojciech M. Grohman, Atam Dhawan

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE
Number of pages1
Volume2
ISBN (Print)0780356756
StatePublished - Dec 1 1999
Externally publishedYes
EventProceedings 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 1999Oct 16 1999

Other

OtherProceedings 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)
CityAtlanta, GA, USA
Period10/13/9910/16/99

All Science Journal Classification (ASJC) codes

  • Bioengineering

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