Advancements in Alzheimer’s Disease Diagnosis: The MRI-CNN Synergy for Early Detection

Alzheimer’s disease is a neurodegenerative condition with profound implications for both physical and mental well-being. Its clinical manifestations encompass a range of high-risk symptoms, including impaired self-care abilities, emotional and behavioral disturbances, cognitive deficits, memory impairment, declining motor skills and coordination, and reduced appetite. If left unaddressed, Alzheimer’s disease can lead to dire outcomes, including fatality. Timely diagnosis is paramount to mitigating the irreversible neurological damage associated with the condition. Raising awareness among at-risk individuals can empower them to proactively adopt preventive measures. Recent research has demonstrated the potential of computational approaches in Alzheimer’s diagnosis. Early detection plays a pivotal role in enhancing treatment efficacy and pharmaceutical interventions. Our study is focused on the early identification of Alzheimer’s disease, with a specific emphasis on the utilization of MRI as a diagnostic input. Employing a 17-layer Convolutional Neural Network (CNN), renowned for its pattern recognition capabilities, we have achieved an impressive accuracy of 99.8%.