Journal of Alzheimers Disease & Parkinsonism
Open Access

Research Article

Feature Extraction of the Alzheimer's Disease Images Using Different Optimization Algorithms

Mohamed M. Dessouky^* and Mohamed A. Elrashidy

Department of Computer Science and Engineering, Faculty of Electronic Engineering, University of Menoufiya, Egypt

Corresponding Author:

Mohamed M. Dessouky
Department of Computer Science and Engineering
Faculty of Electronic Engineering
University of Menoufiya, Egypt
Tel: +2 0100-0580-440
E-mail: mohamed.moawad@el-eng.menofia.edu.eg

Received date: February 29, 2016; Accepted date: April 04, 2016; Published date: April 11, 2016

Citation: Dessouky MM, Elrashidy MA (2016) Feature Extraction of the Alzheimer’s Disease Images Using Different Optimization Algorithms. J Alzheimers Dis Parkinsonism 6:230. doi:10.4172/2161-0460.1000230

Copyright: © 2016 Dessouky MM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Alzheimer’s disease (AD) is a type of dementia that causes problems with memory, thinking and behavior. The symptoms of the AD are usually developed slowly and got worse over time, till reach to severe enough stage which can’t interfere with daily tasks. This paper extract the most significant features from 3D MRI AD images using different optimization algorithms. Optimization algorithms are stochastic search methods that simulate the social behavior of species or the natural biological evolution. These algorithms had been used to get near-optimum solutions for large-scale optimization problems. This paper compares the formulation and results of five recent evolutionary optimization algorithms: Particle Swarm Optimization, Bat Algorithm, Genetic Algorithm, Pattern Search, and Simulated Annealing. A brief description of each of these five algorithms had been presented. These five optimization algorithm had been applied to two proposed AD feature extraction algorithms to get near-optimum number of features that gives higher accuracy. The comparisons among the algorithms are presented in terms of number of iteration, number of features and metric parameters. The results show that the Pattern Search optimization algorithm gives higher metric parameters values with lower number of iteration and lower number of features as compared to the other optimization algorithms.

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