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

Authors:
Peter Mc Leod;Brijesh Verma
Affiliations:
Central Queensland University, Rockhampton, QLD, Australia;Central Queensland University, Rockhampton, QLD, Australia
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

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.