一个基因编程的Rician降噪和可解释的深度学习模型用于阿尔茨海默氏症疾病的严重程度预测
Sajid Ullah Khan1, Abdullah Albanyan2, Mohsin Bilal1
1Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
Journal of Alzheimer's disease : JAD
|November 5, 2024
概括
这项研究引入了一种新的方法,用于减少MRI图像中的Rician噪声,使用基因编程和可解释的深度学习框架来检测阿尔茨海默病. 提出的方法在降低噪音和预测疾病方面表现出卓越的表现.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 磁共振成像 (MRI) 的质量往往会受到噪音的影响,特别是里西亚噪音.
- 这种降解对准确的医学图像分析和疾病诊断构成了重大挑战.
研究的目的:
- 开发一种有效的方法来减少MRI图像中的Rician噪声.
- 通过使用深度学习框架,提高阿尔茨海默病 (AD) 检测的准确性.
主要方法:
- 基因编程 (GP) 技术用于预处理MRI图像,以减少Rician噪声.
- 开发了一个可解释的深度学习框架,利用SHAP值来诊断AD,并结合了对阶级不平衡的过量抽样策略.
主要成果:
- 与现有方法相比,拟议的模型表现出优异的性能,包括DenseNet169,VGGNet15和Inceptionv3.3.
- 这种方法有效地处理了具有有限光谱特征的图像,表现出较低的计算复杂性,并减少了过拟合.
结论:
- 该研究成功地解决了MRI中的Rician噪声,并改善了AD严重程度的预测.
- 总医生用于降低噪音和可解释的AI用于诊断的组合为神经退行性疾病研究提供了一个有前途的方法.
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