Machiraju Jayalakshmi1, *, S. Nagaraja Rao2
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1081-1096, 2020, DOI:10.32604/cmc.2020.011710
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)… More >
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