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Back propagation neural network, PCA, Malignant, Benign
The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Hence, this paper presents Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of the following stages namely, feature extraction, dimensionality reduction, and classification. The features extracted from the magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features such as mean, median, variance, correlation, values of maximum and minimum intensity. In the classification stage, classifier based on Back-Propagation Neural Network has been developed. This classifier has been used to classify subjects as normal, benign and malignant brain tumor images. The results show that BPN classifier gives fast and accurate classification than the other neural networks and can be effectively used for classifying brain tumor with high level of accuracy.
N. Sumitra, Rakesh Kumar Saxena,"Brain Tumor Classification Using Back Propagation Neural Network", IJIGSP, vol.5, no.2, pp.45-50, 2013. DOI: 10.5815/ijigsp.2013.02.07
Mohd Fauzi Othman and Mohd Ariffanan. Probabilistic Neural Network for Brain Tumor Classification. Proceedings of second International Conference on Intelligent Systems, Modelling and Simulation, 2011.
Amir Ehsan Lashkani. A Neural Network based method for brain abnormality detection in MR images using Gabor wavelets. Proceedings of International journal of computer applications, July 2010, 4(7).
Kailash D. Kharat, Pradyumna P. Kulkarni and M.B. Nagari. Brain Tumor classification using Neural Network based methods. International Journal of Computer science and Informatics), 2012, 1(4).
S. Javeed Kussain, T.Satya Savithri, P.V.Sreedevi. Segmentation of Tissues in Brain MRI images using Dynamic Neuro- Fuzzy Technique. International Journal of Soft computing and Engineering, January 2012, 1(6): 2231-2307.
Carlos Arizmendi, Juan Hernandez, Enrique Romero, Alfredo Vellido, Francisco del Pozo. Diagnosis of Brain Tumours from magnetic resonance spectroscopy using wavelets and neural networks.32nd Annual International conference of the IEEE EMBS Buenos Aires , Argentina, 2010 (August 31- September 4).
Ming-Ni Wu, Chia-Chen Lin and Chin-Chen Chang.Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation. Proceedings of 3rd International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), 2:245-250.
K. I. Diamantaras and S. Y. Kung. Principal Component Neural Networks: Theory and Applications Wiley, 1996.
D.F. Specht. Probabilistic Neural Networks. Neural Networks, 1990, 3(1): 109-118.
H. Demuth and M. Beale.Neural Network Toolbox for Use with Mat lab®, 1998.
N.Sivanandam, S.Sumathi, S.N. Deepa. Introduction to Neural Networks Using MATLAB 6.0, McGraw-Hill, 2006.
H. Selvaraj, S. Thamarai Selvi, D. Selvathi, L. Gewali1.Brain MRI Slices Classification Using Least Squares Support Vector Machine, IC-MED, 2007 1(1): 21-33.
R.M. Nishikawa, M.L. Giger, K. Doi, C.J. Vyborny and R.A. Schmidt. Computer Aided Detection of Clustered Micro calcifications in Digital Mammograms. Med. Biol. Eng. Comp, 1995, 33: 174-178.
S. Theodoridis and K. Koutroumbas. Pattern Recognition, Academic Press, San Diego, 1999.