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An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor - 2020

An Integrated Design Of Particle Swarm Optimization (Pso) With Fusion Of Features For Detection Of Brain Tumor

Research Area:  Machine Learning


Tumor in brain is a major cause of death in human beings. If not treated properly and timely, there is a high chance of it to become malignant. Therefore, brain tumor detection at an initial stage is a significant requirement. In this work, initially the skull is removed through brain surface extraction (BSE) method. The skull removed image is then fed to particle swarm optimization (PSO) to achieve better segmentation. In the next step, Local binary patterns (LBP) and deep features of segmented images are extracted and genetic algorithm (GA) is applied for best features selection. Finally, artificial neural network (ANN) and other classifiers are utilized to classify the tumor grades. The publicly available complex brain datasets such as RIDER and BRATS 2018 Challenge are utilized for evaluation of method and attained 99% maximum accuracy. The results are also compared with existing methods which evident that the presented technique provided improved outcomes which are clear proof of its effectiveness and novelty.


Author(s) Name:  Muhammad Sharif,Javaria Amin,Mudassar Raza,Mussarat Yasmin,Suresh Chandra Satapathy

Journal name:  Pattern Recognition Letters

Conferrence name:  

Publisher name:  Elsevier

DOI:  10.1016/j.patrec.2019.11.017

Volume Information:  Volume 129, January 2020, Pages 150-157