Improved chicken swarm optimization to classify dementia MRI images using a novel controlled randomness optimization algorithm

The objective of this research paper is to categorize the magnetic resonance imaging (MRI) images as demented (DEM) or nondemented (ND) using improved chicken swarm optimization technique (ICSO). In literature, CSO technique is widely used to solve numerical optimization and feature selection problem. Using this optimization technique for medical image classification problem will be a pioneering idea. If this technique is directly used to classify the medical images, it provides poor results. Hence, appropriate enhancements are made on the original algorithm using a novel controlled randomness optimization algorithm and control parameter tuning. Cross-over and Rooster selection methods are also implemented in cascaded manner for further performance improvization. All the experiments are made for two cases: with and without statistical features. The brain MRI images of 65 ND and 52 DEM subjects obtained from the Open Access Series of Imaging Studies website are used in this analysis. The ICSO without statistical features provides the highest accuracy of 86.32%, whereas the original chicken swarm optimization technique provides the accuracy of 52.13% and 52.99% with and without statistical features, respectively.