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混合深度学习架构与自适应特征融合用于多阶段阿尔茨海默氏症疾病分类

Ahmad Muhammad¹, Qi Jin¹, Osman Elwasila²

¹School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

|June 26, 2025

概括

一个新的深度学习框架准确地使用自适应特征融合对阿尔茨海默病 (AD) 阶段进行分类. 这种方法整合了来自MRI扫描的局部和全球大脑模式,改善了早期诊断和患者护理.

科学领域:

神经成像和人工智能的人工智能
医学诊断医学诊断医学诊断
神经退行性疾病神经退行性疾病

背景情况:

阿尔茨海默病 (AD) 诊断需要将局部的大脑变化与全球连接相结合.
传统的深度学习模型很难有效地将这些独特的特征类型结合起来,以进行AD分类.

研究的目的:

开发一种新的深度学习框架,用于准确的多阶段阿尔茨海默病 (AD) 分类.
通过动态整合来自MRI扫描的局部结构和全球连接特征来提高诊断精度.

主要方法:

一个使用T1加权MRI扫描进行AD分类的深度学习框架.
一个自适应的功能融合层,采用注意力机制来整合ResNet50 (CNN) 和视觉变压器 (ViT) 功能.
局部结构特征 (ResNet50) 和全球连接模式 (ViT) 的动态融合.

主要成果:

在阿尔茨海默氏症5类 (AD5C) 数据集上获得了99.42%的准确性,超过了以前的基准1.18%.
证明了自适应性特征融合在通过废弃性研究减少错误分类方面的关键作用.
通过对四类数据集的外部验证证实了强大的概括性.

结论:

拟议的框架允许精确的早期诊断阿尔茨海默病 (AD).
整合多层次神经影像特征可促进及时和有针对性的干预,以优化患者护理.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.核磁共振成像 (MRI) 分类的分类适应性特征融合融合是一种适应性特征.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.神经成像是一种神经成像.视觉变压器视觉变压器

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