@Article{cmc.2020.011710,
AUTHOR = {Machiraju Jayalakshmi, S. Nagaraja Rao},
TITLE = {Discrete Wavelet Transmission and Modified PSO with ACO  Based Feed Forward Neural Network Model for Brain Tumour Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1081--1096},
URL = {http://www.techscience.com/cmc/v65n2/39863},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011710}
}