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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
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通过转移学习增强阿尔茨海默氏症疾病分类:微调预训练的算法

Abdelmounim Boudi1, Jingfei He1, Isselmou Abd El Kader2

  • 1School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.

Current medical imaging
|June 14, 2024
PubMed
概括

这项研究使用ResNet50V2深度学习模型来使用MRI图像对阿尔茨海默病 (AD) 进行分类. 该模型实现了96.18%的准确性,证明了其精确AD分期的潜力.

关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.分类 分类 分类 分类.深度学习是一种深度学习.脑部的MRI图像. 脑部的MRI图像. 脑部的MRI图像.转移学习 转移学习

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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:

  • 阿尔茨海默病 (AD) 构成了重大的公共卫生挑战,原因是寿命越来越长.
  • 精确的AD阶段分类至关重要,但由于阶段内的变化和手工分类错误,很难进行.
  • 像ResNet50V2这样的深度学习模型显示出改善图像分类任务的潜力.

研究的目的:

  • 利用ResNet50V2模型,使用MRI扫描进行精确的阿尔茨海默病分类.
  • 调查输入层大小和微调策略对AD分期模型性能的影响.
  • 开发一种精确可靠的方法来分类阿尔茨海默病的阶段.

主要方法:

  • 使用了6400张经过验证的MRI图像的数据集.
  • 转移学习和ResNet50V2模型的微调被用于多类AD分类.
  • 该研究探讨了各种输入层大小和最佳层解策略,包括自定义层和动态学习率降低.

主要成果:

  • 用准确度,AUC,精度,回忆,F1得分和ROC曲线来评估模型性能.
  • 混矩阵可视化模型行为,有助于理解分类性能.
  • 开发的模型在区分AD阶段时达到96.18%的高精度.

结论:

  • 深度学习方法,特别是使用微调的ResNet50V2模型,显著提高了阿尔茨海默病分类的准确性和可靠性.
  • 这种方法对诊断和分期阿尔茨海默病的现实世界临床应用具有前景.
  • 该研究强调了先进的人工智能技术在解决复杂的医疗诊断挑战方面的潜力.