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 paper. The novel approach encapsulates the functionary of Spatially Constrained Fish School Optimization algorithm (SCFSO) 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 MRI (Magnetic Resonance Imaging) 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 (DOI) 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 (T1-W), T2-Weighted (T2-W) and Flair (Fluid Attenuated Inversion Recovery) MRI (Magnetic Resonance Imaging) sequences of various axes coordination. A clear extraction of tumor region from non-tumor region (edema) is rendered by the proposed technique and a therapeutic pre-planning could always be made with such a provision/advantage.

Accurate prediction of brain tumors is vital while getting to the forum of medical image analysis, where precision in decision-making is of paramount importance, and the problems are to be addressed forthwith. For over a decade, innumerable medical imaging techniques using artificial intelligence and machine learning have been promulgated. The present research is intended to develop an algorithm that forges the working principles of the Artificial Bee Colony (ABC) and Interval Type-II fuzzy logic system (IT2FLS) algorithm to delineate the tumor region, which has been encompassed by complex brain tissues. The crux of any therapeutic sequences to be accomplished lies in the decisiveness of the oncologists, where the algorithm presented in this study significantly leverages decision-making through technological intervention. The algorithm proposed has versatility in handling a wide range of image sequences available in the BRATS challenge datasets (2015, 2017, and 2018) that have various levels of barriers, setbacks and hardships in identifying the aberrant regions, and it provides better segmentation outcomes that have been qualitatively validated and justified with metrics, such as DOI, specificity and sensitivity. Augmentation of the visual perception for oncologists is the insignia of this study, which in turn provides better insight and understanding regarding the ailment of the patient.