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)
Read the images and apply wiener filter
Step  2 (2D-DWT)
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.
Step  3. Reduce the features from the coefficients using PPCA
Apply PPCA transformation on the obtained wavelet coefficients.
Put the new dataset in a matrix .
Step  4 (RSE classification using 5 5 cross-validation)
Divide the input data and target data into 5 different groups randomly
Use the th group for test, and other 4 groups to train the RSE algorithm.
Classify test image
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.