使用深度学习技术诊断和分类阿尔茨海默病
1Department of Computer Science, College of Computer in Al-Lith, Umm Al-Qura University, Makkah, Saudi Arabia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了用于阿尔茨海默病诊断的深度学习模型. 在分类痴呆症阶段方面,DenseNet-169实现了更高的准确性,为早期检测提供了潜在的解决方案.
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:
- 阿尔茨海默病是一种进展性神经退行性疾病,影响记忆力,主要在65岁以上的人群中.
- 准确和早期诊断至关重要,但由于手动方法耗时且易出错,因此具有挑战性.
- 现有的诊断技术需要改进,以提高早期检测的准确性.
研究的目的:
- 开发和评估一个深度学习模型,用于准确诊断和分类阿尔茨海默氏症的疾病阶段.
- 为了比较DenseNet-169和ResNet-50卷积神经网络 (CNN) 架构的性能.
- 为阿尔茨海默病的实时分析和分类提供解决方案.
主要方法:
- 使用深度学习,特别是DenseNet-169和ResNet-50 CNN架构.
- 在阿尔茨海默病分类数据集上训练和测试模型.
- 将患者分为四类:非痴呆症,非常轻度痴呆症,轻度痴呆症和中度痴呆症.
主要成果:
- 而DenseNet-169模型表现出卓越的性能,在训练 (0.977) 和测试 (0.8382) 阶段都实现了更高的准确性.
- 该ResNet-50模型实现了0.8870的训练准确度和0.8192.8的测试准确度.
- 丹塞网-169在区分痴呆症不同阶段方面表现出更好的疗效.
结论:
- 深度学习模型,特别是DenseNet-169,显示出对精确和高效的阿尔茨海默病诊断有重大前景.
- 拟议的模型可以帮助早期检测和实时分类,潜在地改善患者管理.
- 进一步的研究可以探索将这些模型集成到临床工作流程中,以获得更广泛的应用.
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