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A New Multi-Expert Decision Combination Algorithm and its Application to the Detection of Circumscribed Masses in Digital Mammograms

Constantinidis, A.S., Fairhurst, Michael, Rahman, Ahmad Fuad Rezaur (2001) A New Multi-Expert Decision Combination Algorithm and its Application to the Detection of Circumscribed Masses in Digital Mammograms. Pattern Recognition, 34 . pp. 1527-1537. ISSN 0031-3203. (doi:10.1016/S0031-3203(00)00088-1) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:467)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1016/S0031-3203(00)00088-1

Abstract

A new multiple expert fusion algorithm is introduced, designated the “augmented behaviour-knowledge space method”. Most existing multiple expert classification methods rely on a large training dataset in order to be properly utilised. The proposed method effectively overcomes this problem as it exploits the confidence levels of the decisions of each classifier. It will be shown that this new approach is advantageous when small datasets are available, and this is illustrated in its application to the detection of circumscribed masses in digital mammograms, with very encouraging results.

Item Type: Article
DOI/Identification number: 10.1016/S0031-3203(00)00088-1
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: J. Harries
Date Deposited: 19 Dec 2007 18:16 UTC
Last Modified: 16 Nov 2021 09:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/467 (The current URI for this page, for reference purposes)

University of Kent Author Information

Fairhurst, Michael.

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