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An Exemplar Pyramid Feature Extraction Based Alzheimer Disease Classification Method

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submitted on 2023-08-31, 06:45 and posted on 2023-09-21, 10:26 authored by Heba Soliman Zaina, Samir Brahim Belhaouari, Tanya Stanko, Vladimir Gorovoy

Dementia is a term used to describe a variety of symptoms related to cognitive impairment in which Alzheimer disease represents 60% – 70% of the cases. As of today, there is no cure for this disease and the only way to prevent any associated medical, economic, and financial impacts or losses is to detect the disease early and work closely with suspected patients to prevent any further progress. In this research, a methodology consisting of 4 modules is proposed: (1) preprocessing, exemplar pyramid along with bi-linear interpolation followed by (2) feature extraction using Gray Level Co-Occurrence Matrix and Local Binary Pattern then (3) concatenation of all extracted features and finally (4) classification of Alzheimer disease stage using deep learning, Multi-Layer Perceptron, in particular. Our proposed method was tested using the MPRAGE structural MRI dataset from Alzheimer Disease Neuro Imaging Initiative (ADNI), and it outperformed other techniques used in the literature review. An accuracy result of 89.80 was reported for multi-class classification of 4 stages of Alzheimer disease (Cognitive Normal, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment and Alzheimer Disease) for both Gray Matter (GM) and White Matter (WM). In term of binary-class classification, we were able to achieve very good results using both GM and WM. By using GM, we were able to distinguish between CN vs EMCI, EMCI vs AD and LMCI vs AD with accuracy results of 96.43%, 90.91% and 95.24% respectively. And using WM, we were able to distinguish between CN vs LMCI with 100% accuracy and EMCI vs LMCI with 95.65% accuracy. While we achieved the same accuracy result of 96.15 using both WM and GM.

Other Information

Published in: IEEE Access
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1109/access.2022.3183185

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2022

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU