Challenges Inherent in Building an Intelligent Paradigm for Tumor Detection Using Machine Learning Algorithms

  • A. S. Shinde
  • V. V. Desai
  • M. N. Chavan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)


Machine learning is at the heart of the big data rebellion sweeping the world today. It is the science of getting the computers to learn without being explicitly programmed as most of the technological systems are in an insurrection to be operated by intelligent machines capable to make the human like verdict to automatically solve human task with perfect results. Artificial intelligence is the heart of every major technological system in the world today. This paper presents the challenges faced to develop a model to acquiesce excellent results and the different techniques of machine learning; here, we also present the broad view of the current techniques used for detection of Brain tumor in computer-aided diagnosis and an innovative method for detection of Brain tumor by artificial intelligence using the algorithm of k-nearest neighbor which is established on the training a model with different values of k and the appropriate distance metrics are used for the distance calculation between pixels.


Machine learning Artificial intelligence Supervised learning Classification Regression Unsupervised learning K-nearest neighbor 


  1. 1.
    Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning, pp. 508. MIT Press, London, U.K (2006). ISBN:978-0-262-03358-9Google Scholar
  2. 2.
    Duch, W., Mańdziuk, J. (eds.): Challenges for computational intelligence. In: Series on Studies in Computational Intelligence, Vol. 63, pp. 488. Springer, New York (2007). ISBN:978-3-540-71983-0Google Scholar
  3. 3.
    Soleimani, V., Vincheh, F.: Improving ant colony optimization for brain MR image segmentation and brain tumor diagnosis. In: First Iranian Conference on Pattern Recognition and Image Analysis (PRIA). IEEE (2013)Google Scholar
  4. 4.
    El-Dahshan, E.-S.A., Mohsen, H.M., Revett, K., Salem, A.-B.M.: Computer-aided diagnosis of human brain tumor through mri: a survey and a new algorithm. Expert Syst. Appl. 4, 5526–5545 (2014), Contents lists available at Science Direct. Scholar
  5. 5.
    Khare, S., Gupta, N., Srivastava, V.: Optimization technique, curve fitting and machine learning used to detect brain tumor in MRI. In: 2014 IEEE International Conference on Computer Communication and Systems (ICCCS 114), 20–21 Feb 2014, Chennai, India.
  6. 6.
    Selvakumar, J.: Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm. In: IEEE Conference on ICAMSE, pp. 186–190 Mar 2012Google Scholar
  7. 7.
    Sudharanil, K., Sarma, T.C., Satya Rasad, K.: Intelligent brain tumor lesion classification and identification from MRI images using kNN technique. In: 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (lCCICCT). 978-1-4673-9825-1/15/$3 l.00 ©2015 IEEE.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.GIT BelgaumBelgaumIndia
  2. 2.ADCETAshta, SangliIndia

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