对多种深度学习模型进行比较分析,并采用缓解驱动的方法来增强阿尔茨海默氏症疾病分类
Areej Y Bayahya1,2, Haneen Banjar3,4,5,6, Omar Talabay7
1Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia. Arigyahya@gmail.com.
Scientific reports
|November 22, 2025
概括
传统的卷积神经网络 (CNN) 在使用MRI扫描进行阿尔茨海默病诊断方面表现出色. 使用ECAResNet269和减轻阶级不平衡的2D网格方法,在痴呆症查中获得了74%的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 通过结构性MRI诊断阿尔茨海默病 (AD) 是一个挑战.
- 深度学习 (DL) 提供了自动化痴呆症检测潜力.
- 缺乏对AD诊断的全面DL模型比较.
研究的目的:
- 系统地比较使用MRI对AD诊断的十个DL架构.
- 评估一种用于核磁共振分析的新二维冠状元切片方法.
- 评估减轻类失衡对诊断绩效的影响.
主要方法:
- 分析了来自1346名患者的14983张T1加权的MRI扫描,使用2D冠状-10切片方法.
- 10个DL架构的系统比较,包括CNN,视觉转换器和囊网络.
- 应用类失衡缓解策略 (SMOTE,成本敏感学习,焦点损失).
主要成果:
- 在ECAResNet269中,通过减轻阶级不平衡,实现了74%的平衡精度.
- 传统的CNN超越了视觉变压器和囊网络,这些网络未能被分类.
- 2D网格方法保留了96%的诊断信息,提供了比3D方法快4.2倍的处理速度.
结论:
- 传统的CNN在神经成像分类任务中最有效.
- ECAResNet269证明了痴呆症查的临床相关性能.
- 2D网格方法平衡了诊断准确性和临床部署的计算效率.
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