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Deep Learners and Deep Learner Descriptors for Medical Applications pp 117–135Cite as

Ensemble of Handcrafted and Deep Learned Features for Cervical Cell Classification

Part of the Intelligent Systems Reference Library book series (ISRL,volume 186)

Abstract

The aim of this work is to propose an ensemble of descriptors for Cervical Cell Classification. The system proposed here achieves strong discriminative power that generalizes well thanks to the combination of multiple descriptors based on different approaches, both learned and handcrafted. For each descriptor, a separate classifier is trained, then the set of classifiers is combined by sum rule. The system we propose here also presents a simple and effective method for boosting the performance of trained CNNs by combining the scores (using sum rule) of multiple CNNs into an ensemble. Different types of ensembles and different CNN topologies with different learning parameter sets are evaluated. Moreover, features extracted from tuned CNNs are used for training a set of Support Vector Machines (SVM). First, we validate our method on two cervical cell-related datasets; then, for more in-depth validation, we test the same system on other bioimage classification problems. Results show that the proposed system obtains state-of-the-art performance in all datasets, despite not being tuned on a specific dataset, i.e. the same descriptors with the same parameters are used in all the datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

Keywords

  • Deep learning
  • Ensemble of classifiers
  • Bioimage classification
  • Cancer data analysis

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Acknowledgements

We gratefully acknowledge the support of: NVIDIA Corporation “NVIDIA Hardware Donation Grant” with the donation of the Titan X used for this research; National Natural Science Foundation of China (81501545).

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padua, via Gradenigo 6/B, 35131, Padua, Italy

    Loris Nanni & Stefano Ghidoni

  2. Department of Information Technology and Cybersecurity, Glass Hall, Room 387, Missouri State University, 901 S. National, Springfield, MO, 65804, USA

    Sheryl Brahnam

  3. Department of Pathology, People’s Hospital of Nanshan District, Shenzhen, 518052, China

    Shaoxiong Liu

  4. Radiology and Imaging Sciences Department, National Institutes of Health Clinical Center, Bethesda, MD, 20892, USA

    Ling Zhang

Authors
  1. Loris Nanni
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  2. Stefano Ghidoni
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  3. Sheryl Brahnam
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  4. Shaoxiong Liu
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  5. Ling Zhang
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Corresponding author

Correspondence to Loris Nanni .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Dr. Loris Nanni

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Prof. Sheryl Brahnam

  3. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, MO, USA

    Assist. Prof. Rick Brattin

  4. Department of Information Engineering, Intelligent Autonomous Systems Lab, Padova, Padova, Italy

    Assist. Prof. Stefano Ghidoni

  5. University of Technology, Sydney, Australia

    Prof. Lakhmi C. Jain

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Nanni, L., Ghidoni, S., Brahnam, S., Liu, S., Zhang, L. (2020). Ensemble of Handcrafted and Deep Learned Features for Cervical Cell Classification. In: Nanni, L., Brahnam, S., Brattin, R., Ghidoni, S., Jain, L. (eds) Deep Learners and Deep Learner Descriptors for Medical Applications. Intelligent Systems Reference Library, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-42750-4_4

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