PMID- 29118808
OWN - NLM
STAT- MEDLINE
DCOM- 20180703
LR  - 20191210
IS  - 1687-5273 (Electronic)
IS  - 1687-5265 (Print)
VI  - 2017
DP  - 2017
TI  - Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet 
      Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier.
PG  - 4205141
LID - 10.1155/2017/4205141 [doi]
LID - 4205141
AB  - 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 
      x 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.
FAU - Jha, Debesh
AU  - Jha D
AUID- ORCID: 0000-0002-8078-6730
AD  - Department of Information and Communication Engineering, Chosun University, 309 
      Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea.
FAU - Kim, Ji-In
AU  - Kim JI
AD  - Department of Information and Communication Engineering, Chosun University, 309 
      Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea.
FAU - Choi, Moo-Rak
AU  - Choi MR
AD  - School of Electrical Engineering, Korea University, 145 Anam-ro, Sungbuk-gu, 
      Seoul 02841, Republic of Korea.
FAU - Kwon, Goo-Rak
AU  - Kwon GR
AUID- ORCID: 0000-0003-3486-8812
AD  - Department of Information and Communication Engineering, Chosun University, 309 
      Pilmun-Daero, Dong-Gu, Gwangju 61452, Republic of Korea.
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
DEP - 20171003
PL  - United States
TA  - Comput Intell Neurosci
JT  - Computational intelligence and neuroscience
JID - 101279357
SB  - IM
MH  - Algorithms
MH  - Artifacts
MH  - Brain/*diagnostic imaging/pathology
MH  - Brain Diseases/*diagnostic imaging/pathology
MH  - Humans
MH  - Image Interpretation, Computer-Assisted/*methods
MH  - *Magnetic Resonance Imaging/methods
MH  - Neuroimaging/methods
MH  - *Principal Component Analysis
MH  - Probability
MH  - Sensitivity and Specificity
MH  - *Wavelet Analysis
PMC - PMC5651159
EDAT- 2017/11/10 06:00
MHDA- 2018/07/04 06:00
PMCR- 2017/10/03
CRDT- 2017/11/10 06:00
PHST- 2017/05/10 00:00 [received]
PHST- 2017/08/07 00:00 [revised]
PHST- 2017/08/23 00:00 [accepted]
PHST- 2017/11/10 06:00 [entrez]
PHST- 2017/11/10 06:00 [pubmed]
PHST- 2018/07/04 06:00 [medline]
PHST- 2017/10/03 00:00 [pmc-release]
AID - 10.1155/2017/4205141 [doi]
PST - ppublish
SO  - Comput Intell Neurosci. 2017;2017:4205141. doi: 10.1155/2017/4205141. Epub 2017 
      Oct 3.