Tumor bagging: a novel framework for brain tumor segmentation using metaheuristic optimization algorithms

• Published in: Multimedia tools and applications Vol. 80; no. 17; pp. 26969 - 26995
• Main Authors: Shivhare, Shiv Naresh, Kumar, Nitin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2021; Springer Nature B.V
• Subjects: Algorithms; Bagging; Brain; Brain cancer; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Electric fields; Glioma; Heuristic methods; Machine learning; Magnetic resonance; Multilayer perceptrons; Multimedia Information Systems; Optimization; Optimization algorithms; Segmentation; Special Purpose and Application-Based Systems; Tumors; Gray wolf optimizer; Spider monkey optimization; Brain tumor segmentation; Training neural network; Magnetic resonance imaging; Artificial electric field optimization algorithm

Brain tumor segmentation is a challenging research problem and several methods in literature have been suggested for addressing the same. In this paper, we propose a novel framework called Tumor Bagging whose objective is to enhance the performance of brain tumor segmentation by combining more than...