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多模型深度学习用于痴呆症检测:解决数据和模型的局限性

Areej Y Bayahya^1,2, Fares Jammal³, Haneen Banjar^1,4,5,6

¹Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Frontiers in neuroscience

|December 11, 2025

概括

这项研究评估了使用结构性MRI扫描诊断痴呆症的深度学习模型. 多模式注意力和3D-CNN模型显示出最佳表现,但对于准确的分类,在精度和概括方面仍然存在挑战.

科学领域:

人工智能的人工智能
医学成像分析医学成像分析
神经科学是一个神经科学.

背景情况:

深度神经网络 (DNN) 已经在医疗成像方面取得了先进的发展,特别是在结构性MRI (sMRI) 分类方面.
目前的DNN模型在痴呆症诊断的预处理和特征提取方面存在局限性.
这项研究通过评估各种痴呆症分类架构来解决这些挑战.

研究的目的:

用sMRI数据评估多种深度学习架构的性能,以对痴呆症,轻度认知障碍 (MCI) 和健康对照进行分类.
根据准确性,特异性和敏感性,确定痴呆症诊断的最有效模型.

主要方法:

评估了八个预训练的卷积神经网络 (CNN),视觉变换器 (ViT),多式联络注意力模型和囊网络 (CapsNet).
使用ADNI的平衡数据集,包括每班10,000次培训,3000次验证和850次测试图像 (痴呆症,MCI,健康对照).
使用sMRI扫描的2D切片进行分类,测量精度,特异性和灵敏度.

主要成果:

3D-CNN和多式联络注意力模型实现了最高的性能 (例如,多式联络注意力的精度为86%,灵敏度为86%).
ViT和CapsNet对阿尔茨海默病 (AD) 的敏感度为100%,但精度低 (AD为43%,其他为0%),表明了阶级失衡问题.
所有模型都表现出性能降低和偏向特定类别.

结论:

当前的深度学习架构在sMRI痴呆症分类方面存在局限性,包括次优特征提取和类偏差.
多模式注意力和3D-CNN模型提供更好的整体性能,但需要提高精度和概括性.
未来的研究应该探索先进的计算机视觉技术和建筑修改,以提高诊断的准确性和效率.

关键词:

在3D-CNN中.在这里,我们可以看到AIAIAI.美国有线电视新闻网 (CNN)这里是ViT ViT ViT 在XAI,XAI就是XAI.美国Caps网络 Caps网络深度神经网络是一个神经网络.痴呆症痴呆症是一种痴呆症.

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