Title: Automatic glioblastoma multiforme detection using hybrid-SVM with improved particle swarm optimisation

Authors: S. Sountharrajan; E. Suganya; M. Karthiga; C. Rajan

Addresses: Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamanagalam, Tamilnadu, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamanagalam, Tamilnadu, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamanagalam, Tamilnadu, India ' Department of Computer Science and Engineering, KS Rangasamy College of Technology, Tiruchengode, Tamil Nadu, India

Abstract: Gliomas is one of the harmful life-frightening brain dementia which occurs with the unnecessary growth of cells in the brain. In medical field, detection of brain tumours is a challenging task. Medical resonance imaging is one of the best techniques to identify the tumours. Gaussian filters are used in pre-processing to increase the image quality. Grey-level matter co-occurrence matrix can be used to extract the features by using spatial relationship between the image pixels. In this paper, an advanced classification technique called hybrid-SVM is introduced to classify the brain image with reduced features. To improve the efficiency hybrid-SVM is used with radial basis function kernel. The proposed algorithm classifies the brain tumour classes intelligently. Hybrid-SVM with improved particle swarm optimisation is used to overcome the drawback of hyperplane selection. The experimental result analysis of the proposed work outstrips the other recent classifiers with 92.75% accuracy rate.

Keywords: gliomas; Gaussian filters; grey-level co-occurrence matrix; improved particle swarm optimisation.

DOI: 10.1504/IJBET.2018.089969

International Journal of Biomedical Engineering and Technology, 2018 Vol.26 No.3/4, pp.353 - 364

Received: 01 Jun 2017
Accepted: 21 Jul 2017
Published online: 26 Feb 2018 *

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