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A New Approach for Analyzing MRI Brain Images using Neuro Fuzzy Model

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IJCA Special Issue on International Conference on Computing, Communication and Sensor Network
© 2013 by IJCA Journal
CCSN2012 - Number 2
Year of Publication: 2013
Authors:
Suchita Goswami
Lalit P. Bhaiya

Suchita Goswami and Lalit P Bhaiya. Article: A New Approach for Analyzing MRI Brain Images using Neuro Fuzzy Model. IJCA Special Issue on International Conference on Computing, Communication and Sensor Network CCSN2012(2):24-28, March 2013. Full text available. BibTeX

@article{key:article,
	author = {Suchita Goswami and Lalit P. Bhaiya},
	title = {Article: A New Approach for Analyzing MRI Brain Images using Neuro Fuzzy Model},
	journal = {IJCA Special Issue on International Conference on Computing, Communication and Sensor Network},
	year = {2013},
	volume = {CCSN2012},
	number = {2},
	pages = {24-28},
	month = {March},
	note = {Full text available}
}

Abstract

It is difficult to identify the abnormalities in brain specially in case of Magnetic Resonance Image brain image processing. Artificial neural networks employed for brain image classification are being computationally heavy and also do not guarantee high accuracy. The major drawback of ANN is that it requires a large training set to achieve high accuracy. On the other hand fuzzy logic technique is more accurate but it fully depends on expert knowledge, which may not always available. Fuzzy logic technique needs less convergence time but it depends on trial and error method in selecting either the fuzzy membership functions or the fuzzy rules. These problems are overcome by the hybrid model namely, neuro-fuzzy model. This system removes essential requirements since it includes the advantages of both the ANN and the fuzzy logic systems. In this paper the classification of different brain images using Adaptive neuro-fuzzy inference systems (ANFIS technology). Experimental results illustrate promising results in terms of classification accuracy and convergence rate.

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