An Intelligent System Based on Back Propagation Neural Network and Particle Swarm Optimization for Detection of Prostate Cancer from Benign Hyperplasia of Prostate

Abstract

Volume 5 • Issue 3 • 1000158 J Health Med Inform ISSN: 2157-7420 JHMI, an open access journal applying weights to the data that are computed. In recent years, the use three layers feed forward neural networks, is the most usual type of feed forward NN, which propagates the information from the input towards the output layer. In addition to these methods, a heuristic optimization algorithm is used to increase the success and speed of these methods. PSO as a heuristic optimization method is successfully applied to train MLPNN. It is proposed to update network weights by reasons of easy implementation and realization, the small number of parameters to be set, and capable of treatment with real numbers, no derivative information. In this study, in order to improve the ability of conventional neural network to escape from a local optimum, the PSO algorithm was used to modify the Network parameter and precision [4,9,10]. Particle Swarm Optimization (PSO) algorithm Particle Swarm Optimization algorithm (PSO) is a randomly optimal algorithm based on swarm intelligence. The algorithm can be used to solve optimization problems. One of the first implementations of PSO was that of training Neural Networks and one key advantage of PSO over other optimization algorithms in training neural networks is its comparative simplicity. As described by Eberhart and Kennedy, the PSO algorithm is an adaptive algorithm based on a social psychological metaphor; a population of individuals adapts by returning stochastically toward previously successful regions in the search space, and is influenced by the successes of their topological neighbors. Each particle in the swarm represents a candidate solution to the optimization problem, and if the solution is made up of a set of variables the particle can correspondingly be a vector of variables. In a PSO system each particle is ''flown'' through the multidimen-sio n al search space, adjusting its position in search space according to its own experience and that of neighboring particles. The particle therefore makes use of the best position encountered by itself and that of its neighbors to position itself toward an optimal solution. The performance of each particle is evaluated using a predefined fitness function, which encapsulates the characteristics of the optimization problem. The main operators of the PSO algorithm are the velocity and the position of the each particle. In each iteration, particles evaluate their positions according to a fitness function [6,11,12]. Then the velocity and the position of the each particle are updated according to below equation 1;