Title CSO-based algorithm with support vector machine for brain tumor's disease diagnosis

Abstract

This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression. The framework consists of four steps: segmentation, feature extraction and feature reduction, classification, finally the parameter values of the classifier are dynamically optimized using the optimization algorithm Chicken Swarm Optimization (CSO) which is a bio-inspired optimization algorithm, and particle swarm optimization (PSO) optimizers to maximize the classification accuracy. We used 80, 100, 150 neuroimages training data set sizes to train the system and 100 out of sample neuroimages to test the system. The proposed system preliminary results demonstrate the efficacy and efficiency of the system to accurately detect and classify the brain tumor in MRI, that motivate us to expand applying of this system on other types of tumors in medical imagery.