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一个高效的基于排名的组合多分类器,用于使用深度学习进行神经退行性疾病分类.

Palak Goyal¹, Rinkle Rani², Karamjeet Singh²

¹Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India. pgoyal60_phd18@thapar.edu.

Journal of neural transmission (Vienna, Austria : 1996)

|September 9, 2024

概括

一种新的深度学习组合方法可以准确预测阿尔茨海默氏症和帕金森病. 这种方法提高了神经退行性疾病的诊断准确性,优于现有的方法.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.深度学习是一种深度学习.一起组合在一起.神经退行性疾病的神经退行性疾病帕金森病是帕金森氏症的一种疾病.

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科学领域:

神经科学是一个神经科学.
人工智能的人工智能
医疗成像医学成像

背景情况:

神经退行性疾病,如阿尔茨海默氏症 (AD) 和帕金森氏症 (PD) 导致神经元逐渐丧失,影响认知和运动功能.
目前的诊断方法,包括机器学习,具有准确性的局限性.
准确和早期诊断对于管理这些衰弱的疾病和改善患者的结果至关重要.

研究的目的:

开发和验证一种新的基于排名的整体方法,使用深度学习来改善AD和PD的诊断.
为了提高神经退行性疾病预测的准确性和概括性.
将拟议的方法与现有的诊断方法进行比较.

主要方法:

一个三阶段的建模程序,涉及数据预处理,使用五种深度学习模型进行分类,以及基于排名的整体策略.
使用磁共振成像 (MRI) 数据集:阿尔茨海默氏症神经成像计划 (ADNI) 针对阿尔茨海默氏症和帕金森氏症渐进性标记者计划 (PPMI) 针对PD.
采用加权策略和排名计算,以从个别深度学习模型中预测集合.

主要成果:

取得的高分类准确度:AD-CN (97.89%),AD-MCI (99.33%),CN-MCI (99.44%),PD-CN (99.22%),PD-SWEDD (97.56%),CN-SWEDD (98.22%).这些分类的准确度均比较高.
证明了有希望的多类分类准确性:97.18%的AD和97.85%的PD.
拟议的深度学习组合方法在二进制和多类分类任务中表现优于现有的方法.

结论:

开发的基于深度学习的组合技术为预测阿尔茨海默氏症和帕金森病提供了具有竞争力和准确的解决方案.
这种方法显著提高了诊断神经退行性疾病的概括性能.
这种方法代表了在早期和准确检测AD和PD的有希望的进步.