Deep Learning Approaches for Early Detection of Alzheimer's Disease using MRI Neuroimaging

Abstract

Alzheimer's disease is a neurodegenerative disorder and one of the most prevalent forms of progressive Dementia. Alzheimer's disease does not have any cure as it leads to brain shrinkage and damage of the brain cells. Early detection can aid in assessing and administering suitable treatment that can slow down disease progression. Progressive monitoring of individuals diagnosed with Mild Cognitive Impairment (MCI) through neuroimaging has gained considerable interest recently for early detection. The most popular neuroimaging used being the Magnetic Resonance Imaging (MRI). The intention of monitoring individuals diagnosed with MCI is that, MCI diagnosed are more likely to get converted to Alzheimer's. Deep learning models have proven to be very effective and shown powerful performance in neuroimaging analytics. Deep learning techniques have been employed over brain MRI for assessing Alzheimer's disease progression and gained immense popularity in recent times due to its commendable performance. In this paper, we present a study on the applications of Deep learning techniques in early detection and progression of Alzheimer's disease. The study focuses on recent advances in the early detection of Alzheimer's using Deep learning models and MRI neuroimaging.