使用MRI组合深度学习来诊断阿尔茨海默病:整合VGG16,MobileNet和InceptionResNetV2模型的功能
Meshrif Alruily1, A A Abd El-Aziz2, Ayman Mohamed Mostafa2
1Computer ScienceDepartment, College of Computer and Information Sciences, Jouf University, Sakaka,Saudi Arabia.
PloS one
|April 7, 2025
概括
这项研究引入了一个深度学习组合模型,用于使用MRI扫描来诊断阿尔茨海默病 (AD). 该模型实现了高精度,改善了早期检测,并支持放射科医生诊断这种神经退行性疾病.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,影响数百万人,其特征是粉样质斑块和神经纤维状结.
- 目前对阿尔茨海默病的诊断方法在准确性方面存在局限性,并且容易误诊.
- 阿尔茨海默病的患病率上升需要先进的诊断工具,以便及时进行干预.
研究的目的:
- 开发和评估一个深度学习 (DL) 整体模型,以通过MRI扫描准确识别阿尔茨海默病 (AD).
- 通过整合多个预训练DL模型 (VGG16,MobileNet,InceptionResNetV2) 的功能来提高诊断精度.
- 提供一种工具,以帮助放射科医生在早期发现AD,并促进及时治疗.
主要方法:
- 通过结合VGG16,MobileNet和InceptionResNetV2.2,创建了一个集体深度学习模型.
- 该模型经过训练,并通过MRI扫描进行评估,以识别阿尔茨海默病标志物.
- 分析了包括精度,特异性,灵敏度,精度和F1分数在内的性能指标.
主要成果:
- DL整体模型实现了97.93%的高诊断精度.
- 特别的性能被认为是98.04%的特异性,95.89%的灵敏性和95.94%的精度.
- 整体方法在识别AD标志物方面超过了当代分类器.
结论:
- 深度学习组合模型为使用MRI诊断阿尔茨海默病提供了强大而准确的方法.
- 开发的模型显示了提高早期检测率和简化放射科医生的诊断工作流程的巨大潜力.
- 这种创新方法代表了对抗阿尔茨海默病的有希望的进展,使得早期干预和治疗成为可能.
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