Classification of Brain MRI using Multi-Cluster Feature Selection and KNN Classifier

Accurate and efficient diagnosis in a short time period is an important part of brain magnetic resonance imaging (MRI) classification. In this paper, we use multi-cluster feature selection (MCFS) method to select efficient features from the primary features for brain MRI classification. The primary features are obtained from a three-level two-dimensional discrete wavelet transform (2D DWT). The selected features are then applied to the K-nearest neighbor (KNN) classifier. We classify the MRI as normal or one of the seven different diseases. The results demonstrate that the proposed method achieves higher accuracy than the other methods in distinguishing different types of disease.