神经网络-AD:一个多模式深度学习框架,用于多类阿尔茨海默病诊断
Saeka Rahman1, Md Motiur Rahman1, Smriti Bhatt2
1School of Engineering Technology, Electrical and Computer Engineering Technology, Purdue University, West Lafayette, IN 47907, USA.
Bioengineering (Basel, Switzerland)
|October 29, 2025
概括
通过融合MRI扫描和临床数据,NeuroNet-AD增强了阿尔茨海默病 (AD) 诊断. 这种新的深度学习模型在对AD和轻度认知障碍 (MCI) 的分类方面取得了高准确性.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病 (AD) 是最常见的痴呆症,影响认知和日常生活.
- 早期诊断AD和轻度认知障碍 (MCI) 对于患者的护理和治疗至关重要.
- 目前用于AD诊断的深度学习模型因数据稀缺和融合多式联络数据的挑战而面临局限性.
研究的目的:
- 开发一个新的多式联络深度学习框架,NeuroNet-AD,用于增强阿尔茨海默病的分类.
- 通过将磁共振成像 (MRI) 与基于临床文本的元数据集成来提高诊断准确性.
- 解决现有模型在处理和融合多式联络数据以检测AD的局限性.
主要方法:
- 提出了NeuroNet-AD,这是一个多模式卷积神经网络 (CNN),集成MRI图像和临床元数据.
- 在ResNet-18骨干中集成的卷积区注意模块 (CBAM) 用于特征聚焦.
- 引入了超导交叉注意力 (MGCA),用于使用多头注意力的有效交叉模式融合.
- 对于文本数据,采用基于集合的特征选择策略.
主要成果:
- 神经网络-AD在多类 (NC,MCI,AD) 中达到98.68%的准确性,在二进制中达到99.13%的准确性 (NC与NC对比). AD) 在ADNI1数据集上的分类.
- 对OASIS-3数据集的外部验证显示了94.10%的多类和98.67%的二元分类准确性.
- 与最先进的模型相比,在阿尔茨海默氏症诊断中表现出卓越的性能.
结论:
- 神经网络-AD有效地整合了多模式数据 (MRI和临床文本) 以准确诊断阿尔茨海默病.
- 拟议的框架,包括CBAM和MGCA,显著提高了分类性能.
- NeuroNet-AD表现出强大的概括能力,优于现有方法,并为早期AD检测提供了一个有前途的工具.
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