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Brain MRI classification is one of the key areas of research. The classification of brain MRI can help radiologists in different brain diseases diagnostics without invasive measures. Brain MRI classification is a difficult task due to the variance and complexity of brain diseases. We have proposed a novel and efficient binary classification model for brain MRI images. The proposed model includes discrete wavelet transform (DWT) used for features extraction, statistical features for diminishing the number of features, and a blended artificial neural network for brain MRI classification. Brain MRI classification with less features is a challenging task. In this paper, we have proposed a novel technique for statical features calculation of approximate RGB images obtained from DWT. We have also proposed a new blended artificial neural network to improve classification accuracy. The proposed technique is compared with other state-of-the-art techniques, and results show that the proposed technique gives better outcomes in terms of accuracy and simplicity.

An Effective Classification Methodology for Brain MRI Classification Based on Statistical Features, DWT and Blended ANN