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Abstract：
Alzheimer\'s disease (AD) is a neurological disorder that significantly impairs cognitive function, leading to memory loss and eventually death. AD progresses through three stages: early stage, mild cognitive impairment (MCI) (middle stage), and dementia. Early diagnosis of Alzheimer\'s disease is crucial and can improve survival rates among patients. Traditional methods for diagnosing AD through regular checkups and manual examinations are challenging. Advances in computer-aided diagnosis systems (CADs) have led to the development of various artificial intelligence and deep learning-based methods for rapid AD detection. This survey aims to explore the different modalities, feature extraction methods, datasets, machine learning techniques, and validation methods used in AD detection. We reviewed 116 relevant papers from repositories including Elsevier (45), IEEE (25), Springer (19), Wiley (6), PLOS One (5), MDPI (3), World Scientific (3), Frontiers (3), PeerJ (2), Hindawi (2), IO Press (1), and other multiple sources (2). The review is presented in tables for ease of reference, allowing readers to quickly grasp the key findings of each study. Additionally, this review addresses the challenges in the current literature and emphasizes the importance of interpretability and explainability in understanding deep learning model predictions. The primary goal is to assess existing techniques for AD identification and highlight obstacles to guide future research.
摘要：
阿尔茨海默病(AD)是一种显著损害认知功能的神经系统疾病,导致记忆丧失，最终死亡。AD经历三个阶段：早期，轻度认知障碍（MCI）（中期），和痴呆症。早期诊断阿尔茨海默病至关重要，可以提高患者的生存率。通过定期检查和手动检查来诊断AD的传统方法具有挑战性。计算机辅助诊断系统（CAD）的进步导致了各种人工智能和基于深度学习的快速AD检测方法的发展。这项调查旨在探索不同的模式，特征提取方法，数据集，机器学习技术，和用于AD检测的验证方法。我们审查了包括Elsevier(45)在内的116篇相关论文，IEEE(25),斯普林格(19),Wiley(6),PLOSOne(5),MDPI(3),世界科学（3）边疆(3),PeerJ(2),Hindawi（2），IO按(1),和其他多个来源（2）。为了便于参考，审查以表格形式列出，让读者快速掌握每一项研究的关键发现。此外,这篇综述解决了当前文献中的挑战,并强调了可解释性和可解释性在理解深度学习模型预测中的重要性.主要目标是评估用于AD识别的现有技术，并强调障碍以指导未来的研究。
