Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.
Biomed Res Int. 2022 Jan 22;2022:8739960. doi: 10.1155/2022/8739960. eCollection 2022.
Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.
阿尔茨海默病（AD）是最常见的神经退行性疾病，导致认知能力的稳步下降。深度学习模型在 AD 的诊断中表现出色，这些模型不需要任何手工制作的特征提取，而传统的机器学习算法则需要。自 2012 年 AlexNet 问世以来，卷积神经网络（CNN）逐渐被医学界用于帮助医生早期诊断 AD。本文探讨了 CNN 在单模态和多模态（两种或多种模态的组合）神经影像学数据上的最新应用，以实现 AD 的分类。在 2021 年 6 月，我们在四个著名的数据库：Google Scholar、IEEE Xplore、ACM Digital Library 和 PubMed 上进行了全面的系统搜索。本研究的目的是检查分类方法在 AD 中的有效性，以分析不同类型的数据集、神经影像学模态、预处理技术和数据处理方法。然而，CNN 在 AD 的分类中取得了巨大的成功；尽管如此，仍然存在很多挑战，特别是由于医学成像数据的稀缺性及其在该领域的可能范围。
