Abstract: Alzheimer’s Disease (AD), the most common form of dementia, is a degenerative disorder of the brain that leads to memory loss. Anatomical changes observed in samples of Alzheimer’s are dramatic shrinkage of the cerebral cortex, fatty deposits in blood vessels, atrophied brain cells, neuro bifilarly tangles and senile plaques. Neuroimaging is a promising area of research for detecting AD. There are multiple brain imaging procedures that can be used to identify abnormalities in the brain, including PET, MRI, and CT scans. Each scan involves a unique technique and detects specific structures and abnormalities in the brain. Inference problem (Confusion) in the diagnosis of AD as the Biomarkers obtained from MRI, PET, SPECT images are similar for the diseases like brain tumor, brain cancer, hormonal disorders etc. Combining the different biomarkers from different neuroimaging techniques at different stages of diagnosis to make it personalize. From the literature review, it is clear that there is need of designing new system for Alzheimer’s disease detection which will be a personalize and help the doctors to detect the AD more accurately, which is reflected in the necessity of developing sensitive and specific biomarkers, specific vector reduction technique and a particular efficient classifier.
Keywords: Alzheimer’s Disease, Neuroimaging, Computer Aided Detection (CAD), Machine Learning.
| DOI: 10.17148/IJARCCE.2020.9322