Classification of Dementia MRI Images Using Hybrid Meta-Heuristic Optimization Techniques Based on Harmony Search Algorithm

Magnetic Resonance Imaging is a commonly used modality to diagnose dementia and there is a massive requisite for an automated MRI image classification algorithm to assist the clinician during diagnosis. The main objective of this research work is to categorize the brain MRI images of patients as demented and non-demented using harmony search based hybrid meta-heuristic optimization algorithms. For this analysis, 65 non-demented and 52 demented subjects collected from Open Access Series of Imaging Studies are used. With appropriate modifications on original algorithms, the classification performance of four meta-heuristic techniques namely Particle Swarm Optimization, Artificial Bee Colony, Ant Colony Optimization, Harmony Search are tested individually as transformation technique based classifier. Then Harmony Search will be hybridized with above mentioned other three meta-heuristic techniques and the classification performance improvement is analyzed. Harmony search based hybrid optimization techniques are widely reported for solving numerical optimization problems, feature extraction, clustering and training neural networks. To the best of our knowledge, there are no reports in the literature regarding the usage of harmony search based hybrid optimization techniques as transformation technique to classify medical images. Particle Swarm Optimization hybridized with Harmony Search algorithm provides the highest accuracy of 84% in dementia MRI image classification.