以人工智能为基础的工具,用于早期检测阿尔茨海默氏症
Shafiq Ul Rehman1, Noha Tarek2, Caroline Magdy2
1College of Information Technology, Kingdom University, Bahrain.
Heliyon
|April 22, 2024
概括
早期阿尔茨海默病 (AD) 检测使用VGG16和海马分析得到改善. 这种方法准确地分类认知状态,帮助对这种不可逆转的神经退行性疾病及时进行干预.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,早期发现对管理至关重要.
- 目前的诊断方法可能是侵入性的,或者对早期认知障碍缺乏敏感性.
- 海马是一个早期受影响的大脑区域,对记忆至关重要,也是阿尔茨海默病的潜在生物标志物.
研究的目的:
- 开发和验证一种新的AI驱动的方法,用于早期检测阿尔茨海默病.
- 利用海马区域和VGG16模型与转移学习来提高分类准确性.
- 为放射科医生创建一个实用的工具,以帮助AD诊断和进展跟踪.
主要方法:
- 使用了阿尔茨海默病神经成像计划 (ADNI) 数据集.
- 使用VGG16卷积神经网络模型与转移学习.
- 集成了先进的图像预处理和逐步数据增强技术,专注于海马.
主要成果:
- 在将患者分类为认知正常 (CN),轻度认知障碍 (MCI) 和AD类别方面取得了高准确性.
- 报告测试准确率为98.17%,验证准确率为97.52%,培训准确率为99.62%.
- 通过渐进的数据增强,证明了增强的模型通用化.
结论:
- 拟议的以海马为重点的VGG16模型与转移学习为早期AD检测提供了一个高度准确的方法.
- 开发的AI工具可以通过网站访问,为放射科医生提供预测能力和可视化工具.
- 这种方法对促进阿尔茨海默病的早期诊断和管理策略作出了重大贡献.
关键词:
阿迪尼阿迪尼是什么意思阿尔茨海默病的疾病阿尔茨海默病的疾病.在认知上是正常的.在海马体内,海马体图像的注册 图像的注册轻度认知障碍 轻度认知障碍在头骨上脱光.转移学习转移学习在VGG16中,VGG16是VGG16中的一个.更多相关视频
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