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Effects of large constituent size in variable neural ensemble classifier for breast mass classification

conference contribution
posted on 06.12.2017, 00:00 by NULL McLeod, Brijesh Verma
This paper proposes a novel ensemble technique for mass classification in digital mammograms by varying the number of hidden units to create diverse candidates. The effects of adding more networks to the ensemble are evaluated on a mammographic database and the results are presented. A classification accuracy of ninety nine percent is achieved.

History

Start Page

525

End Page

532

Number of Pages

8

Start Date

01/01/2013

Finish Date

01/01/2013

ISBN-13

9783642420412

Location

Taegu, Korea

Publisher

Springer

Place of Publication

Heidelberg, Germany

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Name of Conference

ICONIP (Conference)