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International Conference on Neural Information Processing

ICONIP 2020: Neural Information Processing pp 113–125Cite as

An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12532))

Abstract

Gliomas are the most common central nervous system tumors. They represent 1.3% of cancers and are the 15th most common cancer for men and women. For the diagnosis of such pathology, doctors commonly use Magnetic Resonance Imaging (MRI) with different sequences. In this work, we propose a global framework using convolutional neural networks to create an intelligent assistant system for neurologists to diagnose the brain gliomas. Within this framework, we study the performance of different neural networks on four MRI modalities. This work allows us to highlight the most specific MRI sequences so that the presence of gliomas in brain tissue can be classified. We also visually analyze extracted features from the different modalities and networks with an aim to improve the interpretability and analysis of the performance obtained. We apply our study on the MRI sequences that are obtained from BraTS datasets.

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Notes

  1. 1.

    Multi-class classification can also be applied to this dataset for different grades of gliomas.

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Author information

Authors and Affiliations

  1. XLIM Laboratory, University of Poitiers, UMR CNRS 7252, Poitiers, France

    Matthieu Coupet, Thierry Urruty, Pascal Bourdon & Christine Fernandez-Maloigne

  2. I3M, Common Laboratory CNRS-Siemens, University and Hospital of Poitiers, Poitiers, France

    Thierry Urruty, Mathieu Naudin, Pascal Bourdon, Christine Fernandez-Maloigne & Rémy Guillevin

  3. Poitiers University Hospital, CHU, Poitiers, France

    Mathieu Naudin & Rémy Guillevin

  4. DACTIM-MIS/LMA Laboratory University of Poitiers, UMR CNRS 7348, Poitiers, France

    Rémy Guillevin

  5. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, 10520, Thailand

    Teerapong Leelanupab

Authors
  1. Matthieu Coupet
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  2. Thierry Urruty
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  3. Teerapong Leelanupab
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  4. Mathieu Naudin
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  5. Pascal Bourdon
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  6. Christine Fernandez-Maloigne
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  7. Rémy Guillevin
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Corresponding author

Correspondence to Thierry Urruty .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Cite this paper

Coupet, M. et al. (2020). An Empirical Study of Deep Neural Networks for Glioma Detection from MRI Sequences. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12532. Springer, Cham. https://doi.org/10.1007/978-3-030-63830-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-63830-6_10

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