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基于视网膜成像的玻璃眼瘤检测,使用基于极端学习机器的修改优化.

Debendra Muduli1, Rani Kumari2,3, Adnan Akhunzada4

  • 1Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, 751012, India.

Scientific reports
|November 29, 2024
PubMed
概括
此摘要是机器生成的。

使用快速离散曲线转换和经过修改的优化算法与极端学习机器的自动化系统,可以从 fundus 图像中准确地检测 DrDeramus,从而改善早期诊断.

关键词:
在ELM中,可以选择ELM.在FDCT-WRP中使用.青光眼的检测检测仪IOPOP IOPOP IOPOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP IOP在 LDA LDA 中.这就是MOD-POAA.

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 玻璃眼是一种渐进的视神经病变,导致不可逆转的失明.
  • 准确和及时的诊断对于治疗眼症至关重要.
  • 自动计算机辅助诊断 (CAD) 系统可以通过 fundus 图像帮助早期检测.

研究的目的:

  • 开发一个改进的自动化计算机辅助诊断 (CAD) 模型,用于从 fundus 图像对 DrDeramus 的二元分类.
  • 为了提高准确性和减少对青光眼检测的计算时间.
  • 使用可解释AI (XAI) 技术调查开发模型的可解释性.

主要方法:

  • 使用FDCT-WRP (Fast Discrete Curvelet Transform with Wrapping) 来捕获类似曲线的特征,进行特征提取.
  • 通过结合主要组件分析 (PCA) 和线性差异分析 (LDA) 来减少特征.
  • 使用混合模型进行分类:修改的优化算法 (MOD-POA) 优化极端学习机器 (ELM).

主要成果:

  • 拟议的MOD-POA+ELM模型实现了高精度:G1020数据集上的93.25%和ORIGA数据集上的96.75%.
  • 通过FDCT-WRP方法,有效地提取了与眼病分类相关的特征.
  • 七种可解释的人工智能 (XAI) 方法用于模型解释性和验证.

结论:

  • 开发的自动化CAD系统在通过 fundus图像检测青光眼方面表现出高效率.
  • 先进的特征提取,减少和优化技术的结合导致了优越的分类性能.
  • 使用XAI增强了用于临床应用的自动化玻璃眼诊断系统的可信性和可信性.