Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

261
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
261
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

668
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
668

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Deep learning and IoT-based framework for sesame plant identification and weed detection.

Scientific reports·2026
Same author

Retraction notice to "Health Recommendation System using Deep Learning-based Collaborative Filtering" [Heliyon 9 (2023) e22844].

Heliyon·2025
Same author

AI-Driven intrusion detection and prevention systems to safeguard 6G networks from cyber threats.

Scientific reports·2025
Same author

Attention Gated-VGG with deep learning-based features for Alzheimer's disease classification.

Neurodegenerative disease management·2025
Same author

Blockchain based electronic educational document management with role-based access control using machine learning model.

Scientific reports·2025
Same author

Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Computational biology and chemistry·2025
Same journal

An interpretable framework for cancer drug response prediction using integrated drug and multi-omics data with a hybrid Bi-LSTM-GRU network.

Computational biology and chemistry·2026
Same journal

SegMWB: A lightweight deep learning framework for microscopic image classification.

Computational biology and chemistry·2026
Same journal

Integrated omics and virtual screening predict Tabularin as a dual inhibitor of the prognostic microRNAs mir-19a and mir-32 in colorectal cancer.

Computational biology and chemistry·2026
Same journal

In silico characterization of acetyl-CoA carboxylase from Staphylococcus aureus and Escherichia coli: A comparative analysis.

Computational biology and chemistry·2026
Same journal

An optimized cascaded transformer with progressive attention for lung and colon cancer diagnosis from histopathological images.

Computational biology and chemistry·2026
Same journal

From cross cancer transcriptomics to therapeutics: WGX-50 target hub genes in breast cancer and non-small cell lung carcinoma.

Computational biology and chemistry·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

优化启用了ResNet功能,用于阿尔茨海默病检测的转移学习.

Deepthi K Moorthy1, P Chinnasamy1, P Nagaraj2

  • 1Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu 626126, India.

Computational biology and chemistry
|August 16, 2025
PubMed
概括
此摘要是机器生成的。

早期发现阿尔茨海默氏症 (AD) 是至关重要的. 使用优化的ResNet与转移学习的新方法在MRI扫描中检测AD的准确性达到95.37%.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.痴呆症是一种痴呆症.这就是ResNet ResNet.转移学习转移学习摩鱼优化算法 摩鱼优化算法

更多相关视频

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

870
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

相关实验视频

Last Updated: Sep 11, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

870
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

科学领域:

  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能
  • 神经退行性疾病的诊断 神经退行性疾病的诊断

背景情况:

  • 阿尔茨海默病 (AD) 影响全球数百万人,需要早期检测才能有效管理.
  • 医学成像,特别是MRI,对阿兹海默症诊断有希望,但面临着图像复杂性和有限数据的挑战.
  • 准确高效的AD检测方法对于改善患者的治疗结果至关重要.

研究的目的:

  • 提出一种新的,支持优化的ResNet特征提取技术,用于增强阿尔茨海默病检测.
  • 通过结合LeNet和VGG网络来利用转移学习,以提高诊断准确度.
  • 为了解决医学图像分析的挑战,以早期识别AD.

主要方法:

  • 预处理涉及图像大小调整和中间选.
  • 功能提取使用了一种新的Walrus优化算法-残余神经网络 (WOA-ResNet) 进行ResNet训练.
  • 转移学习通过整合LeNet和VGG网络来实现.

主要成果:

  • 拟议的LeNet-VGG方法与WOA-ResNet相结合,在阿尔茨海默病检测方面取得了高精度.
  • 该方法的最大精度为95.37%,灵敏度为97.24%,特异性为93.73%.
  • 支持优化的ResNet功能提取显著提高了诊断性能.

结论:

  • 开发的技术显示出对准确和早期发现阿尔茨海默病的巨大潜力.
  • 将优化算法与深度学习模型相结合,可以增强神经退行性疾病的诊断能力.
  • 这种方法为阿尔茨海默病诊断中的临床应用提供了一个有前途的工具.