使用深度学习方法对阿尔茨海默病阶段的分类,使用McNemar的测试
Begüm Şener1, Koray Acici2, Emre Sümer1
1Department of Computer Engineering, Başkent University, Ankara, Başkent University, Ankara, Turkey.
PeerJ. Computer science
|March 4, 2024
概括
早期诊断阿尔茨海默病 (AD) 对于减缓进展和降低成本至关重要. 深度学习模型从MRI扫描中准确地检测出轻度认知障碍 (MCI) 和AD,显示出早期检测的高潜力.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 早期诊断阿尔茨海默病 (AD) 对于有效的临床管理和减缓疾病进展至关重要.
- 识别轻度认知障碍 (MCI) 是至关重要的,因为它通常在AD之前,使得及时干预.
- 早期检测策略可以显著降低与AD治疗相关的医疗保健成本.
研究的目的:
- 用MRI数据调查深度学习模型在早期发现和分类阿尔茨海默病 (AD),认知正常 (CN) 和轻度认知障碍 (MCI) 的有效性.
- 评估不同深度学习架构 (包括EfficientNetB0,AlexNet和EfficientNet121) 的性能,以区分这些神经状态.
- 评估分类结果的统计学意义,使用McNemar测试的一个变体.
主要方法:
- 利用阿尔茨海默病神经成像计划 (ADNI) 数据集,包括MRI图像.
- 实施了图像分类的深度学习方法,包括一对一 (1对1) 和一对所有 (1对所有) 策略.
- 采用了EfficientNetB0,AlexNet和EfficientNet121模型进行分类任务和McNemar的测试进行统计验证.
主要成果:
- 使用EfficientNetB0 (1对1) 实现了CN对AD分类的98.94%准确性.
- 使用AlexNet (1对所有) 实现了AD与CNMCI分类的99.58%准确性.
- 使用EfficientNet121.1. 获得了MCI与CN分类的98.42%准确度.
结论:
- 深度学习模型显示出在轻度阶段准确检测阿尔茨海默病 (AD) 的巨大潜力.
- 使用基于MRI的深度学习早期和准确地对MCI和AD进行分类是可行的,并且非常准确.
- 该研究证实了深度学习模型在分类AD,MCI和CN状态方面的表现的统计学意义.
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