通过使用6735张大脑MRI图像进行深度学习来对阿尔茨海默病进行分类和诊断
Seyed Mohammad Mousavi1,2, Khadijeh Moulaei3,4, Leila Ahmadian5
1Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Scientific reports
|July 2, 2025
概括
深度卷积神经网络 (CNN) 显著提高了使用MRI数据的阿尔茨海默病诊断准确度. 像InceptionResNetV2这样的模型实现了近乎完美的结果,使得患者更快地进行干预.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 传统的阿尔茨海默病 (AD) 诊断缺乏准确性和速度.
- 延迟诊断阻碍了及时的患者干预和护理.
- 深度卷积神经网络 (CNN) 提供了提高诊断精度的潜力.
研究的目的:
- 采用深层卷积神经网络 (CNN) 来改善阿尔茨海默病的诊断和分类.
- 在大型MRI数据集上评估各种CNN模型的性能.
主要方法:
- 使用了阿尔茨海默氏症MRI预处理数据集 (6735个脑部MRI扫描).
- 预处理和规范化的MRI数据.
- 培训和评估了四个CNN模型:Xception,VGG19,VGG16和InceptionResNetV2.2. 这些模型是:
- 应用概括,超参数调整,早期停止和动态学习率.
主要成果:
- InceptionResNetV2以0.99准确度,F-score,回忆和精度实现了卓越的性能.
- Xception表现出高性能 (0.97 F-score,回忆,精度;96.89%的准确性).
- 开始ResNetV2和VGG19显示了更快的趋同.
- 在轻度/中度痴呆类别中,InceptionResNetV2取得了100%的成绩;Xception和VGG16也表现出色.
结论:
- 深层卷积神经网络显著提高了阿尔茨海默氏症诊断的准确性和效率,而不是传统方法.
- InceptionResNetV2表现出卓越的性能,有可能加速患者干预.
- 对于精确和快速的阿尔茨海默病检测和分类,CNN显示出了前景.
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