Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

中文翻译：

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机器学习和深度学习技术在阿尔茨海默病有效诊断中的系统综述

阿尔茨海默病（AD）是一种与大脑相关的疾病，患者的病情随着时间的推移而恶化。AD 不是任何药物都可以治愈的疾病。阻止脑细胞死亡是不可能的，但在药物的帮助下，可以延缓 AD 的影响。由于并非所有MCI患者都会患AD，因此早期诊断时需要准确诊断轻度认知障碍（MCI）患者是否会转化为AD（即MCI转化型MCI-C）或不会转化为AD（即MCI非转化型MCI-NC）。医生用于诊断阿尔茨海默病的方法有两种：正电子发射断层扫描 (PET) 和磁共振成像 (MRI)。机器学习和深度学习在需要从高维数据中提取信息的计算机视觉领域表现得非常好。研究人员在医学领域使用深度学习模型进行诊断、预后，甚至预测患者在药物治疗下的未来健康状况。本研究是对使用机器学习和深度学习方法对正常认知 (NC) 和阿尔茨海默病 (AD) 进行早期分类的出版物进行的系统回顾。本研究旨在提供两种最常用的 PET 和 MRI 模式的详细信息用于识别 AD，并在使用不同分类器时评估两种模式的性能。