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Detection of Brain Tumor Using Machine Learning Approach

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Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1045))

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Abstract

Tumor in brain is one of the most dangerous diseases which if not detected at the early stages can even risk the life. Currently, the methods used by neurologists for analysis are not completely error free and states that manual segmentation isn’t a good idea. This study presents machine learning based approach for segmentation of brain images and identification of tumor using SVM classification approach which improve the performance, minimize the complexity and works on real time data.

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Author information

Authors and Affiliations

  1. Thapar Institute of Engineering and Technology, Patiala, India

    Chadha Megha & Jain Sushma

Authors
  1. Chadha Megha
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  2. Jain Sushma
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Corresponding author

Correspondence to Chadha Megha .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

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Megha, C., Sushma, J. (2019). Detection of Brain Tumor Using Machine Learning Approach. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_17

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  • DOI: https://doi.org/10.1007/978-981-13-9939-8_17

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9938-1

  • Online ISBN: 978-981-13-9939-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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