Version 1
: Received: 11 October 2023 / Approved: 11 October 2023 / Online: 12 October 2023 (04:19:37 CEST)
How to cite:
Zhao, M. Recent Progresses in Neural Networks for Alzheimer's Disease Detection. Preprints2023, 2023100771. https://doi.org/10.20944/preprints202310.0771.v1
Zhao, M. Recent Progresses in Neural Networks for Alzheimer's Disease Detection. Preprints 2023, 2023100771. https://doi.org/10.20944/preprints202310.0771.v1
Zhao, M. Recent Progresses in Neural Networks for Alzheimer's Disease Detection. Preprints2023, 2023100771. https://doi.org/10.20944/preprints202310.0771.v1
APA Style
Zhao, M. (2023). Recent Progresses in Neural Networks for Alzheimer's Disease Detection. Preprints. https://doi.org/10.20944/preprints202310.0771.v1
Chicago/Turabian Style
Zhao, M. 2023 "Recent Progresses in Neural Networks for Alzheimer's Disease Detection" Preprints. https://doi.org/10.20944/preprints202310.0771.v1
Abstract
This article reviews the introduction of Alzheimer's Disease (AD), neural networks, training and learning of neural networks, applications of neural networks in early diagnosis of AD, applications of neural networks in AD drug discovery, other brain diseases, and challenges faced by AD. First, the paper introduces the background and characteristics of AD. AD is a degenerative neurological disorder characterized by impaired memory, decreased cognitive function, and loss of neurons. These characteristics place a huge burden on the lives and families of patients. Next, the basic principle and structure of neural network are discussed. A neural network is a computational model made up of multiple neurons that can perform tasks by learning and adapting to input data. In particular, the key concepts of neural network hierarchy, activation function and weight adjustment are discussed. Then, the training and learning methods of neural networks are discussed. Common techniques such as backpropagation algorithm and gradient descent optimizer are introduced in detail, as well as the importance of data preprocessing and model evaluation. Next, the paper focuses on the application of neural network in early diagnosis of AD. By extracting features from brain image data, neural networks can automatically identify differences between AD patients and healthy subjects, enabling early diagnosis and intervention. In addition, the application of neural networks in AD drug discovery is also discussed. By analyzing and predicting a database of known drugs, neural networks can help discover potential treatments for AD and speed up the drug discovery process. The paper further explores the application of neural networks in other brain diseases and highlights the challenges faced by AD, such as the lack of reliable biomarkers, complex pathological mechanisms, etc. In summary, this paper presents a systematic overview of AD, neural networks, training and learning of neural networks, applications of neural networks in early diagnosis of AD and drug discovery, and other brain diseases and challenges associated with AD.
Keywords
Alzheimer's disease; neural networks; Training and learning; early diagnosis; drug discovery; brain diseases
Subject
Medicine and Pharmacology, Psychiatry and Mental Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
