Article
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Convolutional Neural Networks for Alzheimer’s Disease
Version 1
: Received: 21 October 2023 / Approved: 23 October 2023 / Online: 23 October 2023 (10:29:48 CEST)
How to cite: Hu, J. Convolutional Neural Networks for Alzheimer’s Disease. Preprints 2023, 2023101435. https://doi.org/10.20944/preprints202310.1435.v1 Hu, J. Convolutional Neural Networks for Alzheimer’s Disease. Preprints 2023, 2023101435. https://doi.org/10.20944/preprints202310.1435.v1
Abstract
Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.
Keywords
Alzheimer's Disease (AD); Convolutional Neural Networks (CNNs); Biomarkers of AD; Early clinical symptoms; Risk factors; Diagnosis of AD; Medical Images
Subject
Medicine and Pharmacology, Neuroscience and Neurology
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.
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