探索转移学习技术,用rs-fMRI对阿尔茨海默病进行分类
Somayeh Abbasabadi1, Parviz Fattahi1, Mahdyeh Shiri2
1Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.
Computers in biology and medicine
|September 23, 2025
概括
像AlexNet,VGG19和ResNet50这样的深度学习模型使用休息状态功能磁共振成像数据准确地区分阿尔茨海默病患者和健康个体. 亚历克斯网实现了最高的分类准确性,证明了其在早期阿尔茨海默氏症诊断方面的潜力.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 阿尔茨海默病 (AD) 是痴呆症的最常见原因,逐渐损害记忆力和认知功能.
- 由于大脑结构和功能的复杂性,对AD的准确诊断仍然具有挑战性.
- 休息状态功能磁共振成像 (rs-fMRI) 提供了一种非侵入性方法来研究神经疾病中的大脑活动.
研究的目的:
- 调查深度学习算法在使用rs-fMRI数据对阿尔茨海默病患者与正常对照进行分类方面的有效性.
- 为了比较VGG19,AlexNet和ResNet50深度学习模型对AD诊断的性能.
- 用准确度,精度,回忆和F1分数来评估分类性能.
主要方法:
- 从阿尔茨海默病神经成像计划 (ADNI) 数据库中获得了97名参与者的rs-fMRI数据 (56名AD,41名对照).
- 数据在被用于分类之前经历了广泛的预处理.
- 转移学习与VGG19,AlexNet和ResNet50模型用于二进制分类.
主要成果:
- 分类准确率为96.91% (VGG19),98.71% (AlexNet) 和98.20% (ResNet50).这些分类的准确率均为96.91% (VGG19),98.71% (AlexNet) 和98.20%.
- 亚历克斯网在所有评估指标上都表现出卓越的表现,包括精度,回忆和F1分数.
- ResNet50通过Grad-CAM可视化提供了更好的解释性,突出显示了临床相关的大脑区域.
结论:
- 深度学习模型,特别是AlexNet,显示出使用rs-fMRI对阿尔茨海默病的准确和自动诊断的巨大潜力.
- 这些发现表明,rs-fMRI与深度学习相结合,可以成为AD检测和研究的宝贵工具.
- 进一步研究模型的解释性,就像ResNet50一样,可以提高AD病理生理学的临床翻译和理解.
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