Amalgamated Approach of Instance and Probability Based Classification for Alzheimer Detection

Abstract

The Alzheimer disorder discernment is one of the paramount concerns of machine learning and image processing. The Alzheimer detection has the numerous phases named as pre-processing, segmentation, feature extraction and classification. The method of Grey-Level Co-occurrence Matrix (GLCM) is implemented to pull out attributes. The amalgamated procedure of k-nearest neighbor and Naïve Bayes is used for classification. The performance is scrutinized with regard to accuracy, precision and recall. The proposed method is accomplished in MATLAB and upshot is ameliorating for Alzheimer detection.