Exploiting of Classification Paradigms for Early diagnosis of Alzheimer’s disease

Abstract

Alzheimer’s disorder is an incurable neurodegenerative disease that ordinarily affects the aged population. Coherent automated assessment methods are essential for Alzheimer's disease diagnosis in early from distinct images modalities using Machine Learning. This article focuses on exploring various feature extraction and classification methods for early detection of AD proposed by researchers and proposes a modern predictive model that includes Voxel based Texture analysis of brain images for extract features and Optimized Classifier Deep Convolution Neural Network (DCNN) employed for enhance accuracy.