基于深度CNN ResNet-18模型,用于阿尔茨海默病检测的注意力和转移学习
Sofia Biju Francis1,2, Jai Prakash Verma1
1Department of Computer Science and Engineering, Institute of Technology, Nirma University, Gujarat, India.
Frontiers in neuroinformatics
|January 23, 2025
概括
这项研究引入了ResNet-18深度学习模型与Squeeze和Excitation块,以改善阿尔茨海默病的检测. 经过SE阻塞的预训练模型显示出在诊断阿尔茨海默病 (AD),认知正常 (CN) 和轻度认知障碍 (MCI) 方面具有卓越的准确性和ROC值.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 神经学 神经学
背景情况:
- 与年龄相关的大脑疾病,包括阿尔茨海默病 (AD),在全球范围内正在增加.
- 阿尔茨海默病通过损害记忆细胞导致不可逆转的认知衰退.
- 深度学习模型面临医疗数据的挑战,例如成本,道德问题,小型数据集和阶级不平衡.
研究的目的:
- 提出一个基于ResNet-18的系统,整合深度卷积和挤压和刺激 (SE) 块,用于阿尔茨海默病的检测.
- 为了最大限度地减少调整参数,并防止使用不平衡的医疗数据过度适应深度学习模型.
- 为了比较预先训练的ResNet-18模型的性能,有和没有SE块.
主要方法:
- 通过深度卷积和挤压和刺激 (SE) 块增强了ResNet-18架构.
- 预先训练的ResNet-18模型与SE区块一起或不用SE区块一起开发.
- 模型性能使用接收器操作特征 (ROC) 曲线和不同超参数的精度来评估.
主要成果:
- 拟议的模型实现了AD的95%ROC,CN的95%和MCI的93%,最高测试准确率为88.51%.
- 使用SE进行预训练的模型表现出卓越的性能:93.26%的准确性和98-99%的ROC值.
- 没有SE的预训练模型实现了92.41%的精度和94-97%的ROC值.
结论:
- 转移学习,特别是包含SE块的预训练模型,对于小型,不平衡的医疗数据集非常有效.
- 开发的模型有效地减少了培训参数,并减轻了与不平衡的医疗数据常见的过度匹配问题.
- 拟议的方法在检测阿尔茨海默病和相关认知障碍方面优于现有的最先进的技术.
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