COGNITIVE DECLINE PREDICTION: LEVERAGING AI TO DETECT ALZHEIMER’S AT EARLY STAGES

Abstract: Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder that predominantly affects the brain, resulting in the gradual deterioration of memory, cognitive functions, and behavior. It represents the leading cause of dementia, a clinical condition characterized by a significant decline in cognitive abilities that interferes with daily functioning. Despite extensive research, the precise etiology of Alzheimer’s disease remains unclear; however, it is widely accepted that a combination of genetic predisposition, environmental influences, and lifestyle factors contribute to its onset and progression.

Pathologically, Alzheimer’s disease is marked by the abnormal accumulation of extracellular amyloid-beta plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. These abnormalities disrupt neuronal communication, impair synaptic function, and ultimately lead to neuronal degeneration and cell death.

In this context, the present study aims to develop an efficient and accurate automated system for the early detection of Alzheimer’s disease using magnetic resonance imaging (MRI) of the brain. The proposed approach leverages Convolutional Neural Network (CNN) architecture to extract relevant features and perform classification, thereby facilitating improved diagnostic support and early intervention strategies.