TY - JOUR

T1 - An Exemplar Pyramid Feature Extraction based Alzheimer Disease Classification Method

AU - SOLIMAN ZAINA, HEBA

AU - BRAHIM BELHAOUARI, SAMIR

AU - Станко, Татьяна Сергеевна

AU - Горовой, Владимир Андреевич

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2022/6/15

Y1 - 2022/6/15

N2 - 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.

AB - 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.

KW - Cognitive normal (CN)

KW - alzheimer disease (AD)

KW - early mild cognitive impairment (EMCI)

KW - exemplar pyramid

KW - gray level co-occurrence matrix (GLCM)

KW - late mild cognitive impairment (LMCI)

KW - local binary pattern (LBP)

KW - multi-layer perceptron (MLP)

UR - http://www.scopus.com/inward/record.url?scp=85132792071&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/dd107c90-5460-3846-94b8-6e39fcd8a4b0/

U2 - 10.1109/ACCESS.2022.3183185

DO - 10.1109/ACCESS.2022.3183185

M3 - Review article

VL - 10

SP - 66511

EP - 66521

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -