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Discrete Wavelet Transmission and Modified PSO with ACO Based Feed Forward Neural Network Model for Brain Tumour Detection

Machiraju Jayalakshmi1, *, S. Nagaraja Rao2
1 Department of Electrical & Computer Engineering, JNTUA Anantapuramu, Andhra Pradesh, India.
2 Department of ECE, G. Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, India.
* Corresponding Author: Machiraju Jayalakshmi. Email: .

Computers, Materials & Continua 2020, 65(2), 1081-1096. https://doi.org/10.32604/cmc.2020.011710

Received 25 May 2020; Accepted 13 July 2020; Issue published 20 August 2020

Abstract

In recent years, the development in the field of computer-aided diagnosis (CAD) has increased rapidly. Many traditional machine learning algorithms have been proposed for identifying the pathological brain using magnetic resonance images. The existing algorithms have drawbacks with respect to their accuracy, efficiency, and limited learning processes. To address these issues, we propose a pathological brain tumour detection method that utilizes the Weiner filter to improve the image contrast, 2D- discrete wavelet transformation (2D-DWT) to extract the features, probabilistic principal component analysis (PPCA) and linear discriminant analysis (LDA) to normalize and reduce the features, and a feed-forward neural network (FNN) and modified particle swarm optimization (MPSO) with ant colony optimization (ACO) to improve the accuracy, stability, and overcome fitting issues in the classification of brain magnetic resonance images. The proposed method achieves better results than other existing algorithms.

Keywords

Discrete wavelet transformation, ant colony optimization, feed-forward neural network, linear discriminant analysis.

Cite This Article

M. Jayalakshmi and S. Nagaraja Rao, "Discrete wavelet transmission and modified pso with aco based feed forward neural network model for brain tumour detection," Computers, Materials & Continua, vol. 65, no.2, pp. 1081–1096, 2020.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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