Utilizing Profound Learning Methods for Alzheimer's Infection Discovery: An Exhaustive Survey

Abstract

Alzheimer’s disease (AD) is a major public health issue around the world. It is a progressive disease that causes memory loss and cognitive decline. Early detection is essential for early intervention and treatment of AD. In recent years, there has been a surge in the use of deep learning techniques to analyze complex medical data. This includes neuroimaging data as well as genetic data. This has led to promising advances in the detection of AD. In this paper, we will review the application of various deep learning methods in the field of Alzheimer’s Disease. We will look at the challenges that traditional diagnostic methods face and how these challenges can be overcome by using deep learning approaches. We will also look at different deep learning architectures and data modalities and techniques used in the field of AD detection. Finally, we will look at limitations and future direction of the field to help researchers and practitioners to develop more accurate and effective AD detection methods. Keywords: Alzheimer's Disease, deep learning, neuroimaging, biomarkers, early detection, diagnosis.