Bio-inspired swarm techniques for thermogram breast cancer detection

Abstract

Bio-inspired swarm techniques are a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. These techniques involving the study of collective behavior in decentralized systems. Such systems are made up by a population of simple agents interacting locally with one other and with their environment. The system is initialized with a population of individuals (i.e., potential solutions). These individuals are then manipulated over many iteration steps by mimicking the social behavior of insects or animals, in an effort to find the optima in the problem space. A potential solution simplifies through the search space by modifying itself according to its past experience and its relationship with other individuals in the population and the environment. Problems like finding and storing foods, selecting and picking up materials for future usage require a detailed planning, and are solved by insect colonies without any kind of supervisor or controller. Since 1990, several collective behavior (like social insects, bird flocking) inspired algorithms have been proposed. The objective of this article is to present to the swarms and biomedical engineering research communities some of the state-of-the-art in swarms applications to biomedical engineering and motivate research in new trend-setting directions. In this article, we present four swarms algorithms including Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO), Moth Flame Optimization (MFO), and Firefly Algorithm Optimization (FA) and how these techniques could be successfully employed to tackle segmentation biomedical imaging problem. An application of thermography breast cancer imaging has been chosen and the scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: normal or non-normal.