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A multilayered ensemble architecture for the classification of masses in digital mammograms

conference contribution
posted on 06.12.2017, 00:00 by NULL McLeodNULL McLeod, Brijesh VermaBrijesh Verma
This paper proposes a technique for the creation of a neural ensemble that introduces diversity through incorporating ten-fold cross validation together with varying the number of neurons in the hidden layer during network training. This technique is utilized to improve the classification accuracy of masses in digital mammograms. The proposed technique has been tested on a widely available benchmark database.

History

Start Page

85

End Page

94

Number of Pages

10

Start Date

01/01/2012

Finish Date

01/01/2012

ISBN-13

9783642351013

Location

Sydney, Australia

Publisher

Springer

Place of Publication

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

Australasian Joint Conference on Artificial Intelligence