Computational Intelligence and Neuroscience / 2017 / Article / Psdc 1

Research Article

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

Pseudocode 1

Pseudocode of the proposed system.
Input: T2-weighted MR brain images.
Parameter: , total number of images
Step  1 (weiner filter)
for
Read the images and apply wiener filter
end
Step  2 (2D-DWT)
For
Read in the image file
Apply the DWT using for the 3rd level using “Haar” wavelet to extract the wavelet coefficients.
A matrix [] is employed to store all the coefficients.
End
Step  3. Reduce the features from the coefficients using PPCA
for
Apply PPCA transformation on the obtained wavelet coefficients.
Put the new dataset in a matrix .
End
Step  4 (RSE classification using 5 5 cross-validation)
Divide the input data and target data into 5 different groups randomly
For
Use the th group for test, and other 4 groups to train the RSE algorithm.
Classify test image
End
Calculate average specificity, sensitivity, and accuracy.

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.