使用MRI进行多类阿尔茨海默氏病分期的进展意识和可解释的CNN转换器框架
Khalaf Alsalem1, Murtada K Elbashir1, Ahmed Omar Alzahrani2
1Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|February 27, 2026
概括
这项研究介绍了DeepAttentionADNet,这是一个新的AI框架,用于使用MRI扫描准确地分类阿尔茨海默病 (AD) 阶段. 该模型提供了高性能和可解释性,有助于了解疾病进展.
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) 呈现出渐进的神经退行,使得通过MRI进行准确的分期具有挑战性.
- 当前的深度学习模型经常忽视疾病进展,表现出评估泄漏或缺乏解释性.
研究的目的:
- 介绍DeepAttentionADNet,这是一个混合CNN-Transformer模型,用于使用MRI进行多类阿尔茨海默氏症病阶段.
- 通过整合进度意识和可解释性来解决现有方法的局限性.
主要方法:
- 集成卷积神经网络 (CNN) 用于特征提取与变压器用于全球上下文建模.
- 采用渐进意识的顺序学习和一致性规范化,以获得强度和捕捉疾病严重程度.
- 使用无泄漏的交叉验证协议和基于变压器的重要性地图进行解释.
主要成果:
- 在阿尔茨海默病MRI数据集上的交叉验证折叠中实现了高和一致的性能.
- 报告的平均F1得分为0.991 ± 0.003和AUROC为0.9998 ± 0.0002.
- 在分类决策中表现出透明度和进度意识.
结论:
- DeepAttentionADNet提供了一个强大的和可解释的解决方案,用于从MRI中分类阿尔茨海默氏症的严重程度.
- 该框架有效地处理多类分类,同时保持透明度和对疾病进展的认识.
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