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Journal of Medical Signals and Sensors، جلد ۱۳، شماره ۲، صفحات ۱۶۵-۱۷۲
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عنوان انگلیسی Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases
چکیده انگلیسی مقاله It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.
کلیدواژه‌های انگلیسی مقاله Brain magnetic resonance imaging classification, feature reduction, k?means algorithm, nonlinear features, radial basis function networks
نویسندگان مقاله | Amir Bazdar
Department of Electrical and Computer Engineering, Urmia University


| Amir Hatamian
1-Department of Electrical and Computer Engineering, Urmia University,3-Department of Electrical Engineering, Islamic Azad University, Khoy Brach, Khoy, Iran


| Javad Ostadieh
Department of Electrical and Computer Engineering, Urmia University


| Javad Nourinia
Department of Electrical and Computer Engineering, Urmia University


| Changiz Ghobadi
Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran University


| Ehsan Mostafapour


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/672
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Short Communications
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