Title: Brain tumour detection and classification using hybrid neural network classifier

Authors: Krishnamurthy Nayak; B.S. Supreetha; Phillip Benachour; Vijayashree Nayak

Addresses: Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal, Karnataka, India ' Department of Electronics and Communication Engineering, Manipal Institute of Technology, Manipal, Karnataka, India ' Department of Computing and Communication, Lancaster University, UK ' Department of Biological Sciences, BITS Pilani, Goa Campus, India

Abstract: Brain tumour is one of the most harmful diseases, and has affected majority of people in the world including children. The probability of survival can be enhanced if the tumour is detected at its premature stage. Moreover, the process of manually generating precise segmentations of brain tumours from magnetic resonance images (MRI) is time-consuming and error-prone. Hence, in this paper, an effective technique is employed to segment and classify the tumour affected MRI images. Here, the segmentation is made with adaptive watershed segmentation algorithm. After segmentation, the tumour images were classified by means of hybrid ANN classifier. The hybrid ANN classifier employs cuckoo search optimisation technique to update the interconnection weights. The proposed methodology will be implemented in the working platform of MATLAB and the results were analysed with the existing techniques.

Keywords: brain tumour; magnetic resonance images; MRI; hybrid ANN; cuckoo search optimisation; adaptive watershed segmentation; preprocessing; feature extraction; GLCM features; SVM-ABC.

DOI: 10.1504/IJBET.2021.10035780

International Journal of Biomedical Engineering and Technology, 2021 Vol.35 No.2, pp.152 - 172

Received: 02 Aug 2017
Accepted: 12 Jan 2018

Published online: 19 Feb 2021 *

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