TY - JOUR
KW - Alzheimer's Disease
KW - Literature Review
KW - Mild Cognitive Impairment
KW - Neuroimaging
KW - Machine Learning
KW - Classification
KW - Deep Learning
AU - Sitara Afzal
AU - Muazzam Maqsood
AU - Umair Khan
AU - Irfan Mehmood
AU - Hina Nawaz
AU - Farhan Aadil
AU - Oh-Young Song
AU - Yunyoung Nam
AB - Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper.
IS - Special Issue on Current Trends in Intelligent Multimedia Processing Systems
M1 - 7
N2 - Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper.
PY - 2021
SP - 26
EP - 38
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Alzheimer Disease Detection Techniques and Methods: A Review
UR - https://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_3.pdf
VL - 6
SN - 1989-1660
ER -