深度,浅度和整体机器学习方法的性能评估,用于对阿尔茨海默病的自动分类
Noushath Shaffi1, Karthikeyan Subramanian1, Viswan Vimbi1
1College of Computing and Information Sciences, University of Technology and Applied Sciences, P.O. Box: 135, Suhar 311, Sultanate of Oman, Oman.
International journal of neural systems
|April 5, 2024
概括
这项研究引入了使用MRI数据进行阿尔茨海默病 (AD) 诊断的机器学习集合分类器,达到96.52%的准确性. 这种方法为深度学习提供了有竞争力的替代方案,尤其是在有限的数据的情况下.
科学领域:
- 医学成像分析 医学成像分析
- 人工智能在医学中的应用
- 神经科学是一个神经科学.
背景情况:
- 人工智能 (AI) 对于计算机辅助诊断 (CAD) 至关重要,深度学习 (DL) 在分类阿尔茨海默病 (AD) 阶段方面显示出前景.
- 传统的机器学习 (ML) 模型可以匹配或超过DL性能,特别是当训练数据有限时.
研究的目的:
- 建议和评估使用磁共振成像 (MRI) 数据进行AD诊断的整体ML分类器.
- 在数据稀缺和数据丰富的场景中,将ML分类器与DL算法的性能进行比较.
主要方法:
- 使用应用到MRI数据的流行的ML模型开发了一个整体分类器.
- 在阿尔茨海默病神经成像计划和成像研究数据集的开放访问系列上评估了ML分类器.
- 与最先进的DL算法比较ML组合性能.
主要成果:
- 拟议的ML组合分类器在AD分类中实现了96.52%的准确性.
- 与最好的个人ML分类器相比,显示出3-5%的性能改善.
- ML分类器在数据稀缺和数据丰富的条件下都表现出有效性.
结论:
- 集体ML分类器通过MRI为AD诊断提供了一个高度准确和数据效率高的方法.
- 本研究提供了根据数据可用性为AD分类选择适当的AI算法的指导.
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