期刊
INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES
卷 6, 期 7, 页码 89-98出版社
INST ADVANCED SCIENCE EXTENSION
DOI: 10.21833/ijaas.2019.07.012
关键词
MRI classification; Discrete wavelet transform; Color moments; Principal component analysis; Feature extraction; Approximation component; Artificial neural network
Many methods have been proposed to classify the MR brain images automatically. We have proposed a method based on a Neural Network (NN) to classify the normality and abnormality of a given MR brain image. This method first employs a median filter to minimize the noise from the image and converted the image to RGB. Then applies the technique of Discrete Wavelet Transform (DWT) to extract the important features from the image and color moments have been employed in the feature reduction stage to reduce the dimension of the features. The reduced features are sent to Feed-Forward Artificial neural network (FF-ANN) to discriminate the normal and abnormal MR brain images. We applied this proposed method on 70 images (45 normal, 25 abnormal). The accuracy of the proposed method of both training and testing images are 95.48%, while the computation time for feature extraction, feature reduction, and neural network classifier is 4.3216s, 4.5056s, and 1.4797s, respectively. (C) 2019 The Authors. Published by IASE.
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