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A Multilayered Ensemble Architecture for the Classification of Masses in Digital Mammograms

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AI 2012: Advances in Artificial Intelligence (AI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7691))

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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.

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Author information

Authors and Affiliations

  1. Central Queensland University, Bruce Highway, Rockhampton, QLD, 4702, Australia

    Peter Mc Leod & Brijesh Verma

Authors
  1. Peter Mc Leod
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  2. Brijesh Verma
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Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, 2052, Sydney, NSW, Australia

    Michael Thielscher

  2. School of Computing and Mathematics, University of Western Sydney, 1797, Penrith South DC, NSW, Australia

    Dongmo Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Leod, P.M., Verma, B. (2012). A Multilayered Ensemble Architecture for the Classification of Masses in Digital Mammograms. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-35101-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35100-6

  • Online ISBN: 978-3-642-35101-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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