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Brain MRI Classification Using Gradient Boosting

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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (MLCN 2020, RNO-AI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12449))

Abstract

Early detection of Isocitrate Dehydrogenase (IDH) mutations can be used in decision making procedures. We demonstrated the role of important features identification using extreme gradient boosting ensemble from MR imagery and their effectiveness in classification of IDH mutations.

In this work, the MR images are first pre-processed using a number of image processing techniques. Then features are extracted from the pre-processed images that are further classified using boosting ensemble. After, removing very high negative and postive as well as zero valued attributes from the extracted feature spaces, an increase in the performance accuracy is observed. The proposed technique is simple yet efficient in classifying IDH mutations from MR imagery. This will help practitioners to noninvasively diagnose and predict IDH wildtype and IDH mutants for grades II, III, and IV.

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Author information

Authors and Affiliations

  1. Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Kingdom of Saudi Arabia

    Muhammad Tahir

Authors
  1. Muhammad Tahir
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Corresponding author

Correspondence to Muhammad Tahir .

Editor information

Editors and Affiliations

  1. Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  2. Lahore University of Management Sciences, Lahore, Pakistan

    Hassan Mohy-ud-Din

  3. University of Pennsylvania, Philadelphia, PA, USA

    Ahmed Abdulkadir

  4. King’s College London, London, UK

    Cher Bass

  5. The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Mohamad Habes

  6. University College London, London, UK

    Jane Maryam Rondina

  7. CUBRIC, Cardiff, UK

    Chantal Tax

  8. IBM Almaden Research Center, San Jose, CA, USA

    Hongzhi Wang

  9. University of Oslo, Oslo, Norway

    Thomas Wolfers

  10. Eli Lilly Pharmaceutical Company, Philadelphia, PA, USA

    Saima Rathore

  11. Symbiosis Institute of Technology, Pune, India

    Madhura Ingalhalikar

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Tahir, M. (2020). Brain MRI Classification Using Gradient Boosting. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-66843-3_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66842-6

  • Online ISBN: 978-3-030-66843-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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