A study of automatic segmentation of White Matter Hyperintensity for detection of Alzheimer’s disease

Abstract

Alzheimer’s disease is a type of neurodegenerative disorders involving a long-term and generally significant decrease in cognitive performance. Age is the main risk factor for neural disorder, and so it is the aged who are highly affected by this neural disorder. Because of the intensity of the spread of this disease on a global level, organizations and researchers are continuing to invest in the early detection and prevention of such disorders, with an emphasis on proper treatment and medication. Cost-efficient and scalable methods for detecting dementia from some of the most extreme ways are required, similar to the early stages of Subjective Memory Loss (SML), to more drastic stages like Mild Cognitive Impairment (MCI) and Alzheimer’s Dementia (AD) itself. The focus of this work is to build a reliable Deep learning algorithm based on the OASIS, ADNI, and WMH challenge dataset for the identification of cognitive impairment (CI).In this paper an elaborate review has been made of the various methodologies and algorithms used in various frameworks to efficiently and automatically segment WMH (White Matter Hyperintensities) in the brain to detect lesions and areas related to various anomalies, Alzheimer’s being one of them.