Advances in Alzheimer’s disease diagnosis with machine learning and deep learning techniques: a comprehensive review

Abstract Alzheimer’s disease (AD) is a chronic neurological disease and one of the main causes of dementia in around the world. Traditional diagnostic techniques have limits in terms of subjectivity and resource availability, despite the fact that early and accurate identification of AD is essential for efficient supervision. Deep learning (DL) and Machine Learning (ML) have emerged as powerful tools in medical imaging and have shown promising results in AD detection. This review provides a comprehensive analysis of the latest developments in ML and DL for AD diagnosis, covering essential data sets such as Alzheimer’s Disease Neuroimaging Initiative (ADNI), Open Access Series of Imaging Studies (OASIS), and Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL), as well as preprocessing techniques that enhance data quality. Here, we review some of the significant AD studies and investigate how ML and DL might assist researchers in making an early diagnosis more accurate.