Automated Brain Abnormality Segmentation using Modified JAYA algorithm based Fuzzy C-Means Clustering

The main risk factors in humans, irrespective of gender or location, are anomalies of the brain, such as tumors and strokes. In healthcare facilities, the degree of disability and underlying cause of brain lesions are typically evaluated by means of regular methods created by trained clinicians. During this procedure, signals and images of the brain are collected in a controlled setting. Selecting an appropriate separation method is essential to getting the desired results. The novel JAYA algorithm, which relies on meta-heuristics, aids in the reclustering of the FCM segmentation results in our suggested techniques, yielding more productive delineation results with clear borders with 86% of Dice Score. However, the exploitation and exploration techniques for searching become more flexible and help identify the optimal solution faster by adjusting the desired function with respect to the accommodating cluster location and population size. Regarding the processing speed, optimal exploitation, and exploratory abilities, the suggested strategy outperforms the other available metaheuristic optimization techniques.