Deep Learning for Alzheimer’s Disease Diagnosis from Brain MRI: Review

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative condition that strongly impacts cognition and quality of life. Accurate and early diagnosis is essential for effective management and treatment planning. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have been successful methods for automatic AD detection from brain Magnetic Resonance Imaging (MRI). This review synthesizes advances in deep learning using MRI for AD, focusing on preprocessing steps (rescaling, grayscale, normalization) and data augmentation techniques that ensure generalization and account for dataset imbalance. Explainable AI is also promoted for its ability to enhance transparency and clinical confidence. By description of strengths and limitations of existing approaches, this paper aims to guide researchers toward the design of accurate, interpretable, and clinically relevant AI systems for diagnosing Alzheimer's disease.