堆叠的基于CNN的多道注意力网络用于阿尔茨海默病检测
Najmul Hassan1, Abu Saleh Musa Miah1, Kota Suzuki1
1School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-0006, Japan.
Scientific reports
|February 17, 2025
概括
这项研究引入了一种新型的堆叠卷积神经网络与通道注意网络 (SCCAN),用于使用MRI扫描进行阿尔茨海默氏症 (AD) 分类. 该SCCAN模型实现了高精度,超过了早期AD检测的现有方法.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经系统疾病,其特点是认知能力下降.
- 目前用于AD分类的深度学习模型因特征无效而面临准确性和效率方面的挑战.
- 使用医学图像进行AD的自动诊断系统对于早期干预至关重要.
研究的目的:
- 提出一个轻量级的堆叠卷积神经网络与通道注意网络 (SCCAN) 改进阿尔茨海默氏病的分类.
- 增强特征提取和选择,以从MRI数据中更有效地识别AD.
- 开发一个深度学习模型,适合在较小的数据集上进行培训,并解决阶级不平衡问题.
主要方法:
- 一个新的SCCAN模型,集成5个CNN模块,用于分层的特征提取.
- 一个频道关注模块,根据频道尺寸选择有影响力的特征.
- 培训和验证各种MRI数据集,包括ADNI1,Kaggle和OASIS-1,解决阶级不平衡问题.
主要成果:
- 该SCCAN模型显示了高的分类准确性: 99.58% (ADNI1), 99.22% (Kaggle) 和99.70% (OASIS-1).
- 该模型具有较小的参数,这使得它在训练中非常有效.
- 在阿尔茨海默病分类中,性能超过了最先进的模型.
结论:
- 拟议的SCCAN模型在基于MRI的阿尔茨海默氏症疾病分类方面取得了重大进展.
- SCCAN为自动AD诊断提供了有效和准确的解决方案.
- 轻量级的设计和高精度使SCCAN成为临床应用的有希望的工具.
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