An Enhanced Approach for Detecting Alzheimer’s Disease

Abstract

Alzheimer’s disease affects most of the elderly in today's world. It directly affects the neurotransmitters and leads to dementia. MRI images can spot brain irregularities related to mild cognitive damage. It can be useful for predicting Alzheimer’s disease, though it is a big challenge. In this research, a novel technique is proposed to find to detect Alzheimer’s disease using Adaboost classifier with a hybrid PSO algorithm. Initially, MRI image features are extracted, and the best features are identified by the curvelet transform and Principal Component Analysis (PCA). Adaboost proposed methods yield greater accuracy than the existing systems for analyzing MRI images and give excellent classification accuracy. To evaluate the proposed method three methods metrics namely accuracy, specificity, and sensitivity are used. Based on the results the proposed methods yield greater accuracy than the existing systems.