Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

A Multi-Objective Randomly Updated Beetle Swarm and Multi-Verse Optimization for Brain Tumor Segmentation and Classification

A Multi-Objective Randomly Updated Beetle Swarm and Multi-Verse Optimization for Brain Tumor... This paper plans to develop the optimal brain tumor classification model with diverse intelligent methods. The main phases of the proposed model are ‘(a) image pre-processing, (b) skull stripping, (c) tumor segmentation, (d) feature extraction and (e) classification’. At first, pre-processing of the image is performed by converting the image from red green blue to gray followed by median filtering. Further, skull stripping is done for removing the extra-meningeal tissue from the head image, which is done by Otsu thresholding. As the main contribution, the tumor segmentation is done by the optimized threshold-based tumor segmentation using multi-objective randomly updated beetle swarm and multi-verse optimization (RBS-MVO). The objective constraints considered for the segmentation of the tumor is entropy and variance. Next, the feature extraction techniques like gray level co-occurrence matrix, local binary pattern and gray-level run length matrix is accomplished to extract the set of features. The classification side uses the combination of neural network (NN) and deep learning model called convolutional neural network (CNN) for tumor classification. The extracted features are subjected to NN, and the segmented image is taken as input to CNN. In addition, the weight function of NN and hidden neurons of CNN is optimized by the RBS-MVO. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Computer Journal Oxford University Press

A Multi-Objective Randomly Updated Beetle Swarm and Multi-Verse Optimization for Brain Tumor Segmentation and Classification

Kumar, Katukuri Arun; Boda, Ravi
The Computer Journal , Volume 65 (4): 24 – Oct 29, 2021
Download PDF
24 pages

Loading next page...
 
/lp/ou-press/a-multi-objective-randomly-updated-beetle-swarm-and-multi-verse-zW4FUEyKox

References (42)

Publisher
Oxford University Press
Copyright
© The British Computer Society 2021. All rights reserved. For permissions, please e-mail: [email protected]
ISSN
0010-4620
eISSN
1460-2067
DOI
10.1093/comjnl/bxab171
Publisher site
See Article on Publisher Site

Abstract

This paper plans to develop the optimal brain tumor classification model with diverse intelligent methods. The main phases of the proposed model are ‘(a) image pre-processing, (b) skull stripping, (c) tumor segmentation, (d) feature extraction and (e) classification’. At first, pre-processing of the image is performed by converting the image from red green blue to gray followed by median filtering. Further, skull stripping is done for removing the extra-meningeal tissue from the head image, which is done by Otsu thresholding. As the main contribution, the tumor segmentation is done by the optimized threshold-based tumor segmentation using multi-objective randomly updated beetle swarm and multi-verse optimization (RBS-MVO). The objective constraints considered for the segmentation of the tumor is entropy and variance. Next, the feature extraction techniques like gray level co-occurrence matrix, local binary pattern and gray-level run length matrix is accomplished to extract the set of features. The classification side uses the combination of neural network (NN) and deep learning model called convolutional neural network (CNN) for tumor classification. The extracted features are subjected to NN, and the segmented image is taken as input to CNN. In addition, the weight function of NN and hidden neurons of CNN is optimized by the RBS-MVO.

Journal

The Computer Journal – Oxford University Press

Published: Oct 29, 2021

Keywords: brain tumor segmentation; skill stripping; optimized threshold-based tumor segmentation; feature extraction; neural network; convolutional neural network; randomly updated Beetle Swarm and multi-verse optimization

There are no references for this article.