Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique

Abstract

Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation. When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain, magnetic resonance imaging (MRI) is a great tool. It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture. Radiologists have a difficult time sorting and classifying tumors from multiple images. Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation (NTKFIBC-IS). Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are processed. Different clinical characteristics are then retrieved and analyzed statistically to identify brain tumors. The use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative research. This dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios, illness detection accuracy, and false-positive rates as well as disease detection time as a function of a picture count. This segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current state-of-the-art approaches.