Title: Analysis and evaluation of classification and segmentation of brain tumour images

Authors: M.P. Thiruvenkatasuresh; V. Venkatachalam

Addresses: Department of Computer Science and Engineering, Excel Engineering College, Namakkal, Tamilnadu, India ' Erode Sengunthar Engineering College, Erode, Tamilnadu, India

Abstract: Apparently, the development of a model to detect the tumour part in brain images is of utmost significance. In the initial phase of our work, brain tumour database images have occurred in the preprocessing module using adaptive median filter technique to gain clarity of the image. In addition to the preprocessing process, feature extraction techniques are applied and extracted to the features then the classification method as support vector machine (SVM) classifier is used to classify the images as normal and abnormal. After classification, the abnormal images are observed for segmentation process using fuzzy c-means (FCM) clustering process along with the occupied optimisation methods. For optimising centroid, the FCM used social spider optimisation (SSO) technique with genetic algorithm. The proposed scheme has attained the maximum accuracy when compared to existing classification technique ANFIS and segmentation technique FCM (GWO).

Keywords: brain tumour images; support vector machine; SVM; fuzzy c-means; FCM; social spider optimisation; SSO; genetic algorithm; GA.

DOI: 10.1504/IJBET.2019.10022217

International Journal of Biomedical Engineering and Technology, 2019 Vol.30 No.2, pp.153 - 178

Received: 13 Oct 2016
Accepted: 18 Dec 2016

Published online: 27 Jun 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article