Early Detection of Alzheimers Disease by Using Deep Learning Models: A Summary and Review

Abstract

Abstract. Alzheimer's disease (AD) is a relentless neurodegenerative disorder that causes severe cognitive decline and devastating memory loss, afflicting millions of people around the world. Early detection is critical for effective intervention and potentially slowing progression of AD and improving patients quality of life. Traditional imaging diagnostic methods often miss the subtle changes of early-stage AD. This study reviews the recent advancements in deep learning techniques for early AD detection, particularly models analyzing neuroimaging data such as MRI, CT scans and PET scans. The study summarized the progression from Convolutional Neural Networks (CNNs) to advanced Transformer models and hybrid approaches; compared the strengths and limitations of both architectures and associated requirements of specific characteristics of the neuroimaging data and the Alzheimer's disease detection task. The integration of these models has led to significant improvements in diagnostic accuracy and early warning capabilities, addressing limitations of conventional methods. This Review aims to contribute to the rapid growing knowledge and application in this field. Additionally, the paper explores the challenges of data heterogeneity and the application of federated learning to enhance model robustness across diverse datasets, offering insights into future research directions and clinical implications.