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Data Augmentation for Building an Ensemble of Convolutional Neural Networks

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Innovation in Medicine and Healthcare Systems, and Multimedia

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 145))

Abstract

Bioimage classification is important in subcellular localization, accurate cell phenotype recognition, and histopathological classification, to name a few applications. In this paper, we propose an ensemble of deep learning methods built using different batch sizes, different learning rates, and different methods of data augmentation. Our main goal is to test different methods of data augmentation for building an ensemble that boosts the performance of Convolutional Neural Networks (CNN). Our method is evaluated on a diverse set of bioimage classification problems, with each represented by a benchmark dataset and with each bioimage classification task representing a typical cellular or tissue-level classification problem. The results on these datasets demonstrate that the proposed ensemble does indeed boost the performance of the standard CNN. The MATLAB code of all the descriptors and experiments reported in this paper is available at https://github.com/LorisNanni.

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Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation with their donation of the Titan XP GPU used in this research.

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padua, Via Gradenigo 6, 35131, Padua, Italy

    Loris Nanni & Gianluca Maguolo

  2. Computer Information Systems, Missouri State University, 901 S. National, 65804, Springfield, MO, USA

    Sheryl Brahnam

Authors
  1. Loris Nanni
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  2. Sheryl Brahnam
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  3. Gianluca Maguolo
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Corresponding author

Correspondence to Sheryl Brahnam .

Editor information

Editors and Affiliations

  1. Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  2. Faculty of Informatics, Reutlingen University, Reutlingen, Baden-Württemberg, Germany

    Alfred Zimmermann

  3. Shoreham-by-sea, UK

    Robert J. Howlett

  4. University of Canberra, Canberra, ACT, Australia

    Lakhmi C. Jain

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Nanni, L., Brahnam, S., Maguolo, G. (2019). Data Augmentation for Building an Ensemble of Convolutional Neural Networks. In: Chen, YW., Zimmermann, A., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare Systems, and Multimedia. Smart Innovation, Systems and Technologies, vol 145. Springer, Singapore. https://doi.org/10.1007/978-981-13-8566-7_6

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