Early detection of Alzheimer’s Disease using Deep Learning

Abstract

alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and causes cognitive impairment. It is the most common cause of dementia, a general term for a decline in cognitive abilities that interfere with daily life. Deep Learning, the subset of Artificial Intelligence is used in the early detection of Alzheimer's Disease. The human-level performance of the Deep Learning algorithm has been effectively shown in different disciplines. There isn’t a specific algorithm that is universal, but various Deep Learning algorithms, are used for the early detection of Alzheimer’s Disease. Researchers developed a blood test that could detect Alzheimer’s Disease promoting compounds in blood before the symptoms emerged. These findings may lead to early diagnostic tests for Alzheimer’s and other neurodegenerative diseases. Through research on the “Early detection of Alzheimer’s Disease using Deep Learning”, we can learn more about the potential of using advanced technology to identify the disease at its earliest stages. It also discusses the challenges and limitations of using Deep Learning for Alzheimer's Disease detection and highlights the need for future research in this area. Additionally, it can provide insights into the progression of the disease and potentially lead to the development of more accurate diagnostic tools. KEYWORDS: Alzheimer’s Disease, neurodegenerative, dementia, Early diagnosis, Deep Learning algorithms