Open Access
ARTICLE
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
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. https://doi.org/10.32604/cmc.2020.011710
Citations