Diagnostic Framework for Automatic Classification and Visualization of Alzheimer’s Disease with Feature Extraction Using Wavelet Transform

Volume 19 No 7 (2021)

M. Anitha , V. Karpagam , P. Tamije Selvy

DOI: 10.14704/nq.2021.19.7.NQ21088

Abstract

Alzheimer’s Disease (AD) is a serious disease that destroys brain and is classified as the most widespread type of dementia. Manual evaluation of image scans relies on visual reading and semi-quantitative investigation of various human brain sections, leading to wrong diagnoses. Neuroimaging plays a significant part in AD detection, using image processing approaches that succeed the drawback of traditional diagnosis methods. Feature extraction is done through Wavelet Transform (WT).

Keywords

Alzheimer’s Disease (AD), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Correlation-based Feature Selection (CFS), Feature Selection, Machine-Learning Methods, Mutual Information (MI), Neuroimaging, Random Forest (RF), Wavelet-based CNN.

Copyright © Neuroquantology

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.