使用卷积神经网络诊断阿尔茨海默病和轻度认知障碍
Sara Ghasemi Dakdareh1, Karim Abbasian1
1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.
Journal of Alzheimer's disease reports
|February 26, 2024
概括
卷积神经网络在诊断阿尔茨海默病和轻度认知障碍方面表现有前途. 亚历克斯网的准确率超过98%,证明了其在早期疾病检测和分类方面的有效性.
科学领域:
- 人工智能在医学中的应用
- 神经系统疾病诊断 神经系统疾病诊断
- 机器学习用于医疗保健
背景情况:
- 阿尔茨海默病和轻度认知障碍在全球影响着超过5000万的老年人.
- 早期诊断对于管理这些神经退行性疾病至关重要.
- 阿尔茨海默病和轻度认知障碍的复杂性带来了诊断挑战.
研究的目的:
- 在健康个体中诊断阿尔茨海默病和轻度认知障碍.
- 评估卷积神经网络 (CNN) 在疾病诊断中的有效性.
- 为了比较不同CNN模型在识别这些条件方面的表现.
主要方法:
- 使用了三个不同的CNN模型:AlexNet,DenseNet和CNN1D-LSTM网络.
- 使用预先训练有素的模型 (AlexNet和DenseNet) 来提高诊断能力.
- 综合多样化的患者数据,进行全面的分析和分类.
主要成果:
- 亚历克斯网络模型实现了最高的诊断准确率,超过98%.
- 丹塞网和CNN1D-LSTM模型的准确率分别为88%和91.89%.
- 高精度表明CNN的潜力可靠的疾病检测.
结论:
- 卷积神经网络显示出诊断阿尔茨海默病和轻度认知障碍的巨大潜力.
- 像AlexNet这样的预训练模型对于准确的疾病分类是有效的.
- 这些发现支持使用CNN用于这些疾病的早期检测和管理.
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