Improved Wavelet Filter Bank Selection for Effective Feature Extraction in Alzheimer Classification

Abstract

Background: Alzheimer’s disease (AD) is the primary reason for health problem. Motivation: Being degenerative and progressive with brain cells that can be intervened by health professionals in case of early recognition. Feature extraction is a technique employed for reduction of dimensionality. The features are generated for a image. The extraction of features has to be done accurately without any loss of information. Methods: In this work, a Cuckoo Search (CS) based Wavelet Filter Bank Selection algorithm for classification of Alzheimer’s has been proposed. The Ada Boost classifier, Random Forest (RF), and Classification and Regression Tree (CART) were used for the identification of the affected patient with Magnetic Resonance Imaging (MRI). Results: From results it can be found that proposed CS-based technique is used in classifying AD compared to conventional techniques.