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Interpretation error and accuracy rate are the focal issues in traditional magnetic resonance image (MRI) classification. In order to decipher this issue accurate automatic detection and classification of images with prior knowledge is projected in this research paper. The principal objective of this research work is to form a classification system that should not be suffered by misclassification results so that it can be exaggerated up to countless years. Keep the requirements in attention, a hybrid technique for automatic classification of MR images with shift-invariant discrete wavelet transform (SIDWT) and adaptive neuro-fuzzy inference system (ANFIS) is advocated in this paper. Two different types of tumour explicitly glioma and Meningioma have been given considerable concentration. Feature extraction and classification are the two main stages in this work. In the first stage, five texture features, Contrast, Correlation, Energy, Homogeneity and Entropy are extracted using Gray Level Co-occurrence Matrix (GLCM). In the second stage, an ANFIS classifier is anticipated. The system was established for proficient in classification with the accuracy rate of 99.8%.Fast and accurate results in a reduced time are the main advantages of the suggested method.
Tumour, Shift-Invariant discrete wavelet Transform, Adaptive Neuro-Fuzzy Inference System, Feature extraction, #