基于深度学习的精确和高效的算法检测阿尔茨海默氏症残疾基于深度学习
Fayez Alfayez1, Sergey Rozov2, Mohamed S El Tokhy2,3,4
1Department of Computer Science and Information, College of Science, Majmaah Univesity, Al Majma'ah 11952, Saudi Arabia, f.alfayez@mu.edu.sa.
概括
这项研究开发了一种具有成本效益的深度学习 (DL) 和计算机辅助检测 (CAD) 系统,用于早期阿尔茨海默氏症 (AD) 诊断. 该系统实现了91%的准确性,为及时干预提供了可靠的工具.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,影响认知和记忆.
- 早期发现AD对于有效的干预和改善患者结果至关重要.
- 传统的诊断方法,如MRI和PET扫描,是昂贵的,并不是广泛的.
研究的目的:
- 开发一种自动化,具有成本效益的数字诊断方法,用于早期AD的识别和分类.
- 利用深度学习 (DL) 和计算机辅助检测 (CAD) 来提高诊断可访问性.
- 创建一个可靠的工具,及时诊断阿尔茨海默病和治疗计划.
主要方法:
- 使用预训练的卷积神经网络 (CNN) 来提取特征.
- 集成的多类支持向量机器 (MSVM) 和人工神经网络 (ANN) 分类器.
- 采用基于纹理的算法来减少特征以提高效率.
主要成果:
- 实现了高性能,准确率为91%,精度为95%,回忆率为90%.
- 确定了七个关键的纹理特征,以区分正常病例与轻度AD阶段.
- 验证了拟议的基于DL的CAD系统的稳定性和有效性.
结论:
- 为早期AD检测和诊断提供了一种可靠和负担得起的解决方案.
- 与现有最先进的模型相比,该系统表现出卓越的性能.
- 建议未来对更大的数据集进行研究,并与其他成像模式集成,以提高精度.
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