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Automated diagnosis of various brain abnormalcies is possible if classification of magnetic resonance (MR) human brain images can be carried out in an efficacious manner. The present paper proposes the development of a new approach for automated diagnosis, which rests on classification of brain magnetic resonance imaging (MRI) techniques. In our present work we propose a method that uses an improved version of orthogonal discrete wavelet transform (DWT) for feature extraction, called Slantlet transform, which can especially be useful to provide superior time localization with simultaneous achievement of shorter supports for the filters. The features, hence, obtained are used to train a support vector machine (SVM) based binary classifier that automatically infers whether the images that of a normal brain or that of a pathological one. An excellent classification ratio of 100% could be achieved for a set of benchmark MR brain images, which is significantly better than the results reported in a recent research work employing combination of different feature extraction and classification tools e.g. wavelet transform, neural networks and SVM.