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Data Augmentation for Building an Ensemble of Convolutional Neural Networks

  • Loris Nanni
  • Sheryl BrahnamEmail author
  • Gianluca Maguolo
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (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.

Keywords

Microscopy imaging classification Deep learning Convolutional neural networks Support vector machines 

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PaduaPaduaItaly
  2. 2.Computer Information SystemsMissouri State UniversitySpringfieldUSA

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