LAUSR

Inaccuracies in anomaly prediction have become an alarming issue in the field of medical image analysis, and these quandaries have burgeoned due to the errors caused by the operator, instrument/device and environment, whereof, these troubles can be unanimously rectified with the advent of a novel segmentation approach proposed through this article. The novel approach encapsulates the functionary of spatially constrained fish school optimization (SCFSO) algorithm and interval type-II fuzzy logic system (IT2FLS) techniques, which resolves the erroneous prediction of anomalies present in various topographical locations in brain subjects of magnetic resonance imaging (MRI) modality. Huge datasets and complex tumor (anomalies) can be intervened and examined with ease by the developed approach, and this could be a proactive measure for being implemented or incorporated in clinical practice for the betterment of both doctors and patients, and it can perpetuate a profound experience to the doctors. The developed SCFSO-IT2FLS technique was applied to BRATS-SICAS dataset, and the evaluation metrics—namely, the Dice overlap index and sensitivity value were delivered as 96 ± 2.1 and 98 ± 1.1 by the proposed technique. These values are better than the conventional techniques and the proposed technique is applicable for the segmentation of T1-weighted, T2-weighted, and fluid attenuated inversion recovery MRI sequences of various axes coordination. A clear extraction of tumor region from nontumor region (edema) is rendered by the proposed technique and a therapeutic preplanning could always be made with such a provision/advantage.