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

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. *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: [email protected] Received February 29, 2016; Accepted April 04, 2016; Published 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. to a source of food, and how bats use echolocation to sense distance, and know the difference between food/prey and background barriers. Different algorithms had been developed to simulate the behavior of these species. These algorithms give fast, robust and near-optimum solutions of the complex optimization problems [5,6]. The first optimization algorithm is the genetic algorithm (GA) which had been introduced in 1975 [7]. This model based on Darwin’s theory of the ‘survival of the fittest’ of natural crossover and recombination, mutation, and selection. There are different applications in science and engineering used GA techniques [8,9]. There are many advantages of GA like its ability to deal with complex problems and parallelism. However, GAs may require long processing time for a near-optimum solution to evolve [5,6]. To reduce the processing time and improve the performance of the solutions, several improvements had been applied on the GA. Also, other optimization algorithms had been introduced which depends on different natural processes. This paper compare the results of GA with other four optimization algorithms. The first algorithm is the Pattern Search (PS) which is a one of direct search family which developed at 1960 [10]. PS is an evolutionary technique that is suitable to solve a variety of optimization problems that lie outside the scope of the standard optimization methods. Generally, PS has the advantage of being very simple in concept, easy to implement and computationally efficient [11,12]. The second algorithm is the Simulated Annealing (SA) which had been proposed at 1983 [13]. The idea of SA came 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