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

Author

Grohman, Wojciech M. ; Dhawan, Atam P.

Author_Institution

Dept. of Bioeng., Toledo Univ., OH, USA

Volume

2

fYear

1999

fDate

36434

Abstract

A novel convex set based neuro-fuzzy algorithm for classification of difficult-to-diagnose instances of breast cancer is described. 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

Keywords

cancer; feature extraction; fuzzy neural nets; fuzzy set theory; image classification; mammography; medical image processing; tumours; backpropagation; breast cancer; classification; convex set based neuro-fuzzy algorithm; convex sets; difficult-to-diagnose microcalcifications; fuzzy neural network; real diagnostic systems; receiver operating characteristic analysis; Backpropagation algorithms; Biomedical engineering; Breast biopsy; Breast cancer; Classification algorithms; Clustering algorithms; Data structures; Fuzzy neural networks; Neural networks; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

[Engineering in Medicine and Biology, 1999. 21st Annual Conference and the 1999 Annual Fall Meetring of the Biomedical Engineering Society] BMES/EMBS Conference, 1999. Proceedings of the First Joint

Conference_Location

Atlanta, GA

ISSN

1094-687X

Print_ISBN

0-7803-5674-8

Type

conf

DOI

10.1109/IEMBS.1999.804296

Filename

804296