Email this article 
Classification of Brain Tumor Using Neural Network
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
Brain tumors classification in magnetic resonance imaging (MRI) is very important in medical diagnosis. Most of the current conventional diagnosis techniques are based on human experience in interpreting the MRI-scan for classification. This paper presents an automated method based on backpropagation neural network (BPNN) for classification of the MRI of a human brain. The proposed method utilizes wavelet transform (WT) as a feature extraction tool of the MRI. The proposed method follows two steps: feature extraction and classification. WT is first employed for decomposing the image into different levels of approximate and detailed coefficients and then these coefficients are fed into a BPNN for further classification and tumor detection. The proposed method has been applied on several MRI scans, and the results showed an acceptable accuracy of classification.
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Keywords
References
W. Dou, , S. Ruan, , Y. Chen, D. Bloyet, and J.M. Constans, A framework of fuzzy information fusion for segmentation of brain tumor tissues on MR images, Image and Vision Computing, 2007.
R.J.E. Merry, Wavelet Theory and Applications: A literature study, Eindhoven University of Technology. 2005
L. Prasad and S. S. Iyengar, Wavelet Analysis with Applications to Image Processing. Boca Raton, FL: CRC Press LLC, 1997, pp.101-115.
T. Logeswari and M.Karnan, An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map, International Journal of Computer Theory and Engineering, 2010.
C. Biradar, Shantkumari, Measurement based Human Brain Tumor Recognition by Adapting Support Vector Machine, IOSR Journal of Engineering, 2013.
L. Singh, R.B.Dubey, Z.A.Jaffery and Zaheeruddin, Segmentation and Characterization of Brain from MR Images Advances in Recent Technologies in Communication and Computing, International Conference, IEEE 2009.
S. Roy, S. K. Bandyopadhyay, Detection and Quantification of Brain Tumor from MRI of Brain and its Symmetric Analysis, Department of Computer Science and Engineering, University of Calcutta, 2012.
I. Maiti and M. Chakraborty, A New Method for Brain Tumor Segmentation Based on Watershed and Edge Detection Algorithms in HSV Colour Model, Computing and Communication Systems (NCCCS), National Conference, 2012.
S. Ghanavati, J. Li, T. Liu, P. S. Babyn, W. Doda,G.Lampropoulos "Automatic brain tumor detection in Magnetic Resonance Images", Biomedical Imaging (ISBI), 9th IEEE International Symposium, 2012.
M. Subashini and S. Kumar Sahoo ," Brain MR Image Segmentation for Tumor Detection using Artificial Neural Networks", School of Electrical Engineering, VIT University Vellore,2013.
D.W. Patterson, Artificial Neural networks: Theory and Application, Prentice Hall.1996.
M. Negnevitsky, Artificial Intelligence. Addison Wesley, 2nd edition. 2005.
R. C. Gonzalez and R. E. Woods, Digital Image Processing 2/E. Upper Saddle River, NJ: Prentice-Hall, 2002, pp. 349-404.
Sasirekha, N., Kashwan, K.R., A robust and hybrid Rician noise estimation scheme for magnetic resonance images, (2014) International Review on Computers and Software (IRECOS), 9 (2), pp. 247-254.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
