Deep learning-based Alzheimer's disease diagnosis: a comprehensive review Alzheimer's disease (AD) is a progressive neurological disorder that affects memory, thinking, and behavior. It is the most common form of dementia, with global prevalence expected to rise significantly in the coming decades. Early detection is crucial for effective management and treatment. This review article explores the latest advancements in deep learning (DL) techniques and their applications in medical image analysis, particularly in the diagnosis of AD. AD is characterized by the accumulation of abnormal proteins, such as amyloid and tau, in the brain. These proteins form plaques and tangles that disrupt communication between nerve cells, leading to cognitive decline. The disease progresses gradually, starting with mild symptoms that worsen over time. Early diagnosis is essential for predicting future health outcomes and initiating appropriate interventions. Traditional methods for AD diagnosis include clinical evaluation, cognitive tests, and neuroimaging techniques such as CT and MRI. While these methods are useful, they are not always conclusive. Deep learning techniques offer a promising alternative by enabling the analysis of medical images to detect early signs of AD. This review discusses the key findings and recommendations from recent research on DL techniques in AD diagnosis, highlighting their potential to improve early detection and treatment outcomes. The review also explores the various types of modern techniques for 3D brain MRI-based AD diagnosis, emphasizing their role in advancing the field of AD research and treatment.Deep learning-based Alzheimer's disease diagnosis: a comprehensive review Alzheimer's disease (AD) is a progressive neurological disorder that affects memory, thinking, and behavior. It is the most common form of dementia, with global prevalence expected to rise significantly in the coming decades. Early detection is crucial for effective management and treatment. This review article explores the latest advancements in deep learning (DL) techniques and their applications in medical image analysis, particularly in the diagnosis of AD. AD is characterized by the accumulation of abnormal proteins, such as amyloid and tau, in the brain. These proteins form plaques and tangles that disrupt communication between nerve cells, leading to cognitive decline. The disease progresses gradually, starting with mild symptoms that worsen over time. Early diagnosis is essential for predicting future health outcomes and initiating appropriate interventions. Traditional methods for AD diagnosis include clinical evaluation, cognitive tests, and neuroimaging techniques such as CT and MRI. While these methods are useful, they are not always conclusive. Deep learning techniques offer a promising alternative by enabling the analysis of medical images to detect early signs of AD. This review discusses the key findings and recommendations from recent research on DL techniques in AD diagnosis, highlighting their potential to improve early detection and treatment outcomes. The review also explores the various types of modern techniques for 3D brain MRI-based AD diagnosis, emphasizing their role in advancing the field of AD research and treatment.