A Study on Traditional and Deep Learning Approaches for Automatic Alzheimer's Disease Diagnosis

The review article centers on Alzheimer’s disease (AD), a neurodegenerative ailment that is widely widespread and can be extremely detrimental to individuals as well as their families. Early and accurate AD detection is crucial for timely intervention and improved patient outcomes. The primary objective of this review is to evaluate the current state of research in the early detection and categorization of AD, emphasizing the utilization of both traditional and deep learning techniques utilizing positron emission tomography (PET) and magnetic resonance imaging (MRI) modalities. A systematic search of prominent databases was conducted to identify relevant literature, which included studies related to AD early detection, classification, and image preprocessing. The review highlights significant advancements in AD early detection, primarily driven by deep learning techniques applied to preprocessed images, demonstrating high diagnostic accuracy. However, challenges such as data scarcity, interpretability issues, and the need for model optimization persist. The review underscores the importance of collaborative efforts among researchers, clinicians, data scientists, and policymakers to pave the way for more accurate and efficient AD diagnosis. Ultimately, this review marks a significant step toward early and effective AD detection, offering hope for enhanced patient care and outcomes.