Alzheimer's Disease Diagnosis - A Critical Appraisal of Technique

Alzheimer's disease (AD), a progressive neurological disorder primarily affecting the elderly populations, underscores the importance of early identification to manage its adverse effects. Traditionally, clinical evaluations and neuropsychological tests are used for diagnosis, but advancements in neuroimaging and biomarker technologies pave the way for new classification approaches. This review aims to provide a comprehensive overview of recent developments in AD identification. It begins with a fundamental understanding of the disease and then explores various classification techniques, including machine learning (ML), deep learning (DL), and transfer learning methods. Through a critical analysis of these approaches, their efficacy in accurately diagnosing the disease is assessed, highlighting their strengths and limitations. ML and DL methods, particularly when incorporating multiple biomarkers, show promise in enhancing diagnostic accuracy. However, further research is necessary to validate their applicability and robustness across various populations.