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Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network

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Abstract

In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutional neural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Further, comparisons with other popular CNNs objectively as well as quantitatively in this challenging classification problem is undertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain \(96.5\%\) average classification accuracy for our network and for higher cross validation folds other networks perform similarly with a higher accuracy of \(98.0\%\). Deep CNNs could extract significant features from the GBM histopathology images with high accuracy. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is very promising and with the availability of large scale histopathological image data the deep CNNs are well suited in tackling this challenging problem.

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Authors and Affiliations

  1. Graduate School of Engineering, Mie University, 1577 Kurima-machiya, Tsu, Mie, 514-8507, Japan

    Asami Yonekura, Hiroharu Kawanaka & Haruhiko Takase

  2. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, 45229, USA

    V. B. Surya Prasath & Bruce J. Aronow

  3. Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, OH, 45267, USA

    V. B. Surya Prasath & Bruce J. Aronow

  4. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, 45221, USA

    V. B. Surya Prasath

Authors
  1. Asami Yonekura
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  2. Hiroharu Kawanaka
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  3. V. B. Surya Prasath
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  4. Bruce J. Aronow
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  5. Haruhiko Takase
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Correspondence to V. B. Surya Prasath.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Yonekura, A., Kawanaka, H., Prasath, V.B.S. et al. Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network. Biomed. Eng. Lett. 8, 321–327 (2018). https://doi.org/10.1007/s13534-018-0077-0

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  • DOI: https://doi.org/10.1007/s13534-018-0077-0

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