Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 11
Page No. 3625 - 3629

MRI Technique Based Detection and Classification of Brain Tumor using Support Vector Machine (SVM) and k-Nearest Neighbor (kNN)

Authors : HassanJassim Motlak

References

Alfonse, M. and A.B.M. Salem, 2016. An automatic classification of brain tumors through MRI using support vector machine. Egypt. Comput. Sci. J., 40: 11-21.
Direct Link  |  

Chandrakala, D. and S. Sumathi, 2014. Image classification based on color and texture features using frbfn network with artificial bee colony optimization algorithm. Intl. J. Comput. Appl., 98: 19-29.
CrossRef  |  Direct Link  |  

Ghotekar, B. and K.J. Mahajan, 2016. Brain tumor detection and classification using SVM. National Conf. Innovative Trends Sci. Eng., 4: 180-182.
Direct Link  |  

Gunn, S.R., 1998. Support vector machines for classification and regression. MSc Thesis, School of Electronics and Computer Science, University of Southampton, Southampton, England.

Kaushik, D., U. Singh, P. Singhal and V. Singh, 2014. Brain tumor segmentation using genetic algorithm. Intl. J. Comput. Appl., 5: 13-15.
Direct Link  |  

Kumar, A. and B. Mahavir, 2015. A novel approach for brain tumor detection using support vector machine k-means and pca algorithm. Intl. J. Comput. Sci. Mob. Comput., 4: 457-474.
Direct Link  |  

Madheswaran, M. and D.A.S. Dhas, 2015. Classification of brain MRI images using support vector machine with various Kernels. Biomed. Res., 26: 505-513.
Direct Link  |  

Mahmoud, A. and U.B. Obaidellah, 2017. Artificial intelligence techniques for cancer detection and classification: Review study. Eur. Sci. J., 13: 342-370.
CrossRef  |  Direct Link  |  

Motlak, H.J. and S.I. Hakeem, 2017. Detection and classification of breast cancer based-on terahertz imaging technique using artificial neural network and k-nearest neighbor algorithm. Intl. J. Appl. Eng. Res., 12: 10661-10668.
Direct Link  |  

Moulick, H.N. and M. Ghosh, 2013. Medical image processing using a simd array processor and neural networks. Intl. J. Eng. Inventions, 2: 56-64.
Direct Link  |  

NIST., 2018. Neuro imaging and surgical technologies lab. National Institute of Standards and Technology, Gaithersburg, Maryland, USA. http://nist.mni.mcgill.ca/

Nichat, A.M. and S.A. Ladhake, 2016. Brain tumor segmentation and classification using modified FCM and SVM classifier. Intl. J. Adv. Res. Comput. Commun. Eng., 5: 73-76.
Direct Link  |  

Nithyapriya, G. and C. Sasikumar, 2014. Detection and segmentation of brain tumors using AdaBoost SVM. Intl. J. Innovative Res. Comput. Commun. Eng., 2: 2323-2328.
Direct Link  |  

Pan, R., S. Zhao and J. Shen, 2010. Terahertz spectra applications in identification of illicit drugs using support vector machines. Procedia Eng., 7: 15-21.
CrossRef  |  Direct Link  |  

Suhag, S. and L.M. Saini, 2015. Automatic brain tumor detection and classification using svm classifier. Proceedings of the ISER 2nd International Conference on ISER, July 19, 2015, Singapore, ISBN:978-93-85465-51-2, pp: 55-59.

Wang, S., Y. Zhang, T. Zhan, P. Phillips and Y.D. Zhang et al., 2016. Pathological brain detection by artificial intelligence in magnetic resonance imaging scanning (invited review). Prog. Electromagnet. Res., 156: 105-133.
CrossRef  |  Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved