Tumour region detection in MR brain images using MFCM based segmentation and Self Accommodative JAYA based optimization

Many medical image-based diagnostics, particularly the diagnosis of brain tumours in Magnetic Resonance Imaging (MRI), heavily rely on multi-region segmentation. This work's major objective is to improve the multi-region detection performance by combining a modified Fuzzy C-Means (FCM) with a self-accommodative JAYA (SAJAYA) algorithm. Due to its capacity to choose the number of cluster heads in the FCM stage and population suitability in the optimization stage, this technique is more successful and considerably facilitates the precise MR brain image segmentation. To achieve the best performance, SAJAYA is employed to optimize segmentation variables and reduce the overall computation time and complexity. The proposed algorithm segments the different informative sections, such as cerebrospinal fluid, grey matter, and white matter, which will be most helpful to investigate and characterize the tumour. The experiment's findings show that the suggested algorithm is successful in terms of sensitivity, specificity, accuracy and other benchmark metrics.