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Research Article

JCR. 2020; 7(18): 2847-2855


A SURVEY ON DATA HANDLING TECHNIQUES ON NEUROIMAGING DATA FOR ALZHEIMER DISEASE CLASSIFICATION

Monika Sethi, Dr. Sachin Ahuja, Dr. Vinay Kukreja.

Abstract
Alzheimer Disease (AD) is the most widely recognized degenerative disorder, which grows gradually
and causes brain cells to die. It is one of the common causes of dementia leading to continuous decline in
behavioral, social and thinking abilities that disrupts the sufferer to function independently. For AD classification,
various AI (ML) approaches have been proposed using multimodalities such as MRI, PET, fMRI, and DTI
Neuroimaging as an input. However, it is difficult to compare the different studies for classification performance
because of number of components involvement like subjects/cohorts selection, preprocessing techniques or input
data to the network and the network model itself. Specifically, it is difficult to find which component of the method
(preprocessing techniques, input data management techniques, feature extraction or the classification algorithm)
yields the best improvement in the performance if any. So, this paper aimed to classify various inputs data handling
methods to the network and a comparative analysis was done on these techniques on the basis of their advantages
and disadvantages and the performance of the network.

Key words: Alzheimer Disease, Neuroimaging, Slice, Patch, Region of Interest, Voxel



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