Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier
Joint Authors
Kwon, Goo-Rak
Jha, Debesh
Kim, Ji-In
Choi, Moo-Rak
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-03
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease.
Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD) of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models.
Magnetic resonance imaging (MRI) is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach.
In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT) for feature extraction, probabilistic principal component analysis (PPCA) for dimensionality reduction, and a random subspace ensemble (RSE) classifier along with the K-nearest neighbors (KNN) algorithm as a base classifier to classify brain images as pathological or normal ones.
The proposed methods provide a significant improvement in classification results when compared to other studies.
Based on 5×5 cross-validation (CV), the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.
American Psychological Association (APA)
Jha, Debesh& Kim, Ji-In& Choi, Moo-Rak& Kwon, Goo-Rak. 2017. Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1140941
Modern Language Association (MLA)
Jha, Debesh…[et al.]. Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-11.
https://search.emarefa.net/detail/BIM-1140941
American Medical Association (AMA)
Jha, Debesh& Kim, Ji-In& Choi, Moo-Rak& Kwon, Goo-Rak. Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-11.
https://search.emarefa.net/detail/BIM-1140941
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140941