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Brain tumor detection is an important task in medical field because it provides anatomical information of abnormal tissues in brain which helps the doctors in treatment planning and patient follow-up. In this paper an approach for detection and specification of anomalies present in brain images is proposed. The idea is to combine two metaphors: Neural Network and Fuzzy Logic. These two metaphors are combined in one system called Hybrid Neuro-Fuzzy system. This system enjoys the benefits of both Artificial Neural network system and Fuzzy Logic system and eliminates their limitations. The Neuro-Fuzzy system combines the learning power of Artificial Neural Network system and explicit knowledge representation of fuzzy inference system. The proposed system consists of four stages: data collection through various repository sites or hospitals, Pre processing of various brain images, Feature extraction using Gray Level Co-occurrence Matrix (GLCM) and classification of brain images through Hybrid Neuro-Fuzzy System. Experimental results illustrates promising results in terms of classification accuracy, specificity and sensitivity.
Goswami et al. (Tue,) studied this question.