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A Comprehensive Analysis of MRI Based Brain Tumor Segmentation Using Conventional and Deep Learning Methods

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Intelligent Computing Systems (ISICS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1187))

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Abstract

Brain tumor segmentation plays an important role in clinical diagnosis for neurologists. Different imaging modalities have been used to diagnose and segment brain tumor. Among all modalities, MRI is preferred because of its non-invasive nature and better visualization of internal details of the brain. However, MRI also comes with certain challenges like random noise, various intensity levels, and in-homogeneity that makes detection and segmentation a difficult task. Manual segmentation is extremely laborious and time consuming for the physicians. Manual segmentation is also highly dependent on the physician’s domain knowledge and practical experience. Also, the physician may not be able to see details at the pixel level and may only notice the tumor if it is more prominent and obvious. Therefore, there is a need for brain tumor segmentation techniques that play major role in perfect visualization to assist the physician in identifying different tumor regions. In this paper, we present recent advancements and comprehensive analysis of MRI-based brain tumor segmentation techniques that used conventional machine learning and deep learning methods. We analyze different proposed conventional and state-of-the-art methods in chronological order using Dice similarity, specificity, and sensitivity as performance measures.

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Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS University, Islamabad, Pakistan

    Hikmat Khan

  2. Department of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea

    Syed Farhan Alam Zaidi

  3. Faculty of Computer Information Science (CIS), Higher Colleges of Technology, Sharjah Women’s Campus, Sharjah, UAE

    Asad Safi & Shahab Ud Din

Authors
  1. Hikmat Khan
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  2. Syed Farhan Alam Zaidi
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  3. Asad Safi
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  4. Shahab Ud Din
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Corresponding author

Correspondence to Asad Safi .

Editor information

Editors and Affiliations

  1. Autonomous University of Yucatán, Mérida, Mexico

    Carlos Brito-Loeza

  2. Autonomous University of Yucatán, Mérida, Mexico

    Arturo Espinosa-Romero

  3. Autonomous University of Yucatán, Mérida, Yucatán, Mexico

    Anabel Martin-Gonzalez

  4. Higher Colleges of Technology, Sharjah, United Arab Emirates

    Asad Safi

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Khan, H., Alam Zaidi, S.F., Safi, A., Ud Din, S. (2020). A Comprehensive Analysis of MRI Based Brain Tumor Segmentation Using Conventional and Deep Learning Methods. In: Brito-Loeza, C., Espinosa-Romero, A., Martin-Gonzalez, A., Safi, A. (eds) Intelligent Computing Systems. ISICS 2020. Communications in Computer and Information Science, vol 1187. Springer, Cham. https://doi.org/10.1007/978-3-030-43364-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-43364-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43363-5

  • Online ISBN: 978-3-030-43364-2

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