A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images

Brain tumor is the most severe nervous system disorder and causes significant damage to health and leads to death. Glioma was a primary intracranial tumor with the most elevated disease and death rate. One of the most widely used medical imaging techniques for brain tumors is magnetic resonance imaging (MRI), which has turned out the principle diagnosis system for the treatment and analysis of glioma. The brain tumor segmentation and classification process was a complicated task to perform. Several problems could be more effectively and efficiently solved by the swarm intelligence technique. In this paper, the fuzzy brain-storm optimization algorithm for medical image segmentation and classification was proposed, a combination of fuzzy and brain-storm optimization techniques. Brain-storm optimization concentrates on the cluster centers and provides them the highest priority; it might fall in local optima like any other swarm algorithm. The fuzzy perform several iterations to present an optimal network structure, and the brain-storm optimization seems promising and outperforms the other techniques with better results in this analysis. The BRATs 2018 dataset was used, and the proposed FBSO was efficient, robust and mainly reduced the segmentation duration of the optimization algorithm with the accuracy of 93.85%, precision of 94.77%, the sensitivity of 95.77%, and F1 score of 95.42%.