Classification of MRI Brain Images Using Neuro Fuzzy Model

It is difficult to identify the abnormalities in brain specially in case of Magnetic Resonance Image brain image processing. This paper presents a hybrid technique for the classification of MRI human brain images. The proposed hybrid technique consists of three stages namely feature extraction, feature reduction and classification. The feature extraction and reduction is done by Principal Component Analysis and the classification is done by a hybrid Neurofuzzy classifier (ANFIS). ANFIS classifier combines the merits of both the neuro classifier and the fuzzy classifier and overcomes the demerits of both the classifiers. 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, neurofuzzy 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) is done. Experimental results illustrate promising results in terms of classification accuracy and convergence rate.