Zhao Zhen, Chuah Joon Huang, Lai Khin Wee, Chow Chee-Onn, Gochoo Munkhjargal, Dhanalakshmi Samiappan, Wang Na, Bao Wei, Wu Xiang
Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Front Comput Neurosci. 2023 Feb 6;17:1038636. doi: 10.3389/fncom.2023.1038636. eCollection 2023.
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.
阿尔茨海默病（AD）是一种神经退行性疾病，可导致老年人记忆力衰退和认知功能受损。这种不可逆转且具有毁灭性的认知衰退给患者和社会带来了巨大负担。到目前为止，尚无能够治愈AD的有效治疗方法，但早期AD的进程可以减缓。早期准确检测对治疗至关重要。近年来，基于深度学习的方法在阿尔茨海默病诊断方面取得了巨大成功。本文的主要目的是回顾一些用于利用磁共振成像（MRI）对AD进行分类和预测的流行传统机器学习方法。本文回顾的方法包括支持向量机（SVM）、随机森林（RF）、卷积神经网络（CNN）、自动编码器、深度学习和Transformer。本文还回顾了广泛使用的特征提取器以及卷积神经网络的不同类型输入形式。最后，本综述讨论了类别不平衡和数据泄露等挑战。它还讨论了关于预处理技术、深度学习、传统机器学习方法、新技术和输入类型选择的权衡与建议。
