Title: A hybrid approach to diagnosis mammogram breast cancer using an optimally pruned hybrid wavelet kernel-based extreme learning machine with dragonfly optimisation

Authors: P. Kumara Guru Diderot; N. Vasudevan

Addresses: Department of Electronic and Communication, Hindustan University, Chennai, Tamilnadu, India ' Department of Electronics and Communication, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, India

Abstract: The detection of breast cancer is difficult at the initial stage because the cancerous tumours are rooted in the common breast tissue structures. The objective of this research is to model a breast cancer prediction system with a novel machine learning approach based on wavelets to classify mammogram images as benign, malignant and normal. The prediction of breast cancer for the diagnosis process is made by the proposed algorithm Hybrid Optimally Pruned Wavelet Kernel-based Extreme Learning Machine (HOP-WKELM). Initially, the input is pre-processed for noise reduction using a Kuan filter. Then a quantum evolutionary algorithm (QEA) is applied to segment the cancer part in a mammogram image and extract its features. The extracted features are classified using HWKELM classifier. The HWKELM utilised the dragonfly swarm behaviour-based optimisation (DSBO) approach to optimise the parameters of kernel functions. The proposed strategies achieved a maximum accuracy of 98.8% and maximum precision of 98.1% when compared with AdaBoost systems.

Keywords: breast cancer; hybrid wavelet kernel-based extreme learning machine; grey-level co-occurrence matrix; GLCM; Gabor; local binary pattern; LBP; dragonfly swarm behaviour-based optimisation; DSBO.

DOI: 10.1504/IJCAET.2021.114495

International Journal of Computer Aided Engineering and Technology, 2021 Vol.14 No.3, pp.408 - 425

Received: 30 Jun 2018
Accepted: 19 Sep 2018

Published online: 10 Mar 2021 *

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