Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning, pp. 508. MIT Press, London, U.K (2006). ISBN:978-0-262-03358-9
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:978-3-540-71983-0
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)
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. www.elsevier.com/locate/eswa. https://doi.org/10.1016/j.eswa.2014.01.021
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. https://doi.org/10.1109/icccs.2014.7068202
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 2012
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. https://doi.org/10.1109/iccicct.2015.7475384
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shinde, A.S., Desai, V.V., Chavan, M.N. (2018). Challenges Inherent in Building an Intelligent Paradigm for Tumor Detection Using Machine Learning Algorithms. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-8569-7_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8568-0
Online ISBN: 978-981-10-8569-7
eBook Packages: EngineeringEngineering (R0)