Brain Tumors Diagnosis and Prediction Based on Applying the Learning Metaheuristic Optimization Techniques of Particle Swarm, Ant Colony and Bee Colony

摘要

Brain tumors are intensively studied and many techniques and algorithms have been proposed to extract the features of the brain MRI images and diagnose the tumors. The different techniques are distinguished and favored based on their accuracy and speed. This led to apply optimization methods to improve them. In this paper, we apply three methaheuristic optimization methods which recently gained much interest. These are: the Binary Particle Swarm Optimization (B-PSO), the Ant colony Travel salesman problem (ACO-TSP) and the Artificial Bee colony optimization (ABCO-BFS). These methods are carried out in two main steps. The first step is the segmentation process of the brain MRI images using K-means cluster and the second steps is the features extraction from them. We compare the results of these methods to obtain the best solution for feature extraction. The group method of data handling (GMDH) is applied to classify the extracted features for the tumors diagnosis. Prediction is also carried on by applying the GMDH method for any future complications. The accuracy of classification and prediction is obtained to be range from 88.9% to 98.8%.