Computational Intelligence and Neuroscience / 2017 / Article / Tab 5

Research Article

Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Table 5

Classification comparison (DS-66, DS-160, and DS-255).

ApproachesFeatureRunAccuracy (%)

DWT + SVM + POLY [24]4761DS-66DS-160DS-255
DWT + SVM + RBF [24]4761598.0097.1596.37
DWT + PCA + -NN [4]7598.0097.3396.18
DWT + PCA + FNN + ACPSO [32]19598.0097.5496.79
DWT + PCA + FNN + SCABC [33]195100.0098.7597.38
DWT + PCA + BPNN + SCG [7]195100.0098.9397.81
DWT + PCA + KSVM [5]195100.0098.2997.14
RT + PCA + LS-SVM [34]95100.0099.3898.82
SWT + PCA + IABAP-FNN [11]710100.0098.8898.43
WT + PCA + ABC-SPSO-FNN [11]710100.0099.4499.18
WE + NBC [35]71092.5899.6299.02
DWT + PCA + ADBRF [17]135100.0099.3098.44
DWT + SUR + ADBSVM [18]75100.0099.2298.43
FRFE + DP-MLP + ARCBBO [16]1210100.0099.1998.24
FRFE + BDP-MLP + ARCBBO [16]1210100.0099.3198.12
DWT + PCA + RSE135100.0099.5798.90
DWT + PPCA + RSE (proposed)135100.00100.0099.20

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