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在OCT图像中使用GLCM纹理功能与机器学习和CNN方法进行增强的AMD检测.

Loganathan R1, Latha S1

  • 1Department of Electronics and Communication Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu District, Tamil Nadu, India.

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|January 8, 2025
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概括

这项研究提高了使用视网膜图像的纹理分析来检测与年龄相关的黄斑变性 (AMD). 机器学习与灰色级别共发生矩阵 (GLCM) 功能显著提高了诊断准确性.

关键词:
在GLCM中,GLCM是指GLCM.与年龄相关的黄斑变性.机器学习算法的算法眼科 眼科 眼科随机的森林随机的森林视网膜图像 视网膜图像

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科学领域:

  • 眼科医生 眼科 眼科
  • 医疗成像医学成像
  • 计算机科学 计算机科学

背景情况:

  • 与年龄相关的黄斑变性 (AMD) 是全球失明的主要原因,严重影响视力敏度和预期寿命.
  • 随着AMD患病率的增加,需要在诊断和预后工具方面取得进展,以改善患者的治疗结果.
  • 目前的诊断方法需要改进,以提高准确性和效率.

研究的目的:

  • 在预处理的视网膜图像中改善与年龄有关的黄斑变性 (AMD) 的诊断.
  • 评估灰色水平共发生矩阵 (GLCM) 功能在AMD检测中的纹理分析的有效性.
  • 为了比较不同功能集和机器学习模型的性能,用于AMD分类.

主要方法:

  • 使用光学连贯断层扫描 (OCT) 图像数据集进行分析.
  • 采用灰色水平共发生矩阵 (GLCM) 功能,包括对比度,不相似度,相关性,能量和均性,用于纹理分析.
  • 应用监督机器学习 (ML) 算法和卷积神经网络 (CNN) 技术,比较GLCM特征,选择的GLCM特征和灰度像素特征 (GSF).

主要成果:

  • 使用GSF特征的模型显示精度较低 (例如23-54%).
  • GLCM具有显著改进的准确性,随机森林 (RF) 的准确性高达98%,CNN的准确性高达83%.
  • 精选的细灰级共发生矩阵 (SFGLCM) 特性产生了最佳性能,在RF和CNN模型中达到98%的OCTID检测准确度.

结论:

  • 在AMD检测方面,SFGLCM和GLCM功能显著优于GSF,提高了准确性和概括性.
  • 该研究强调了机器学习的潜力,特别是通过GLCM进行纹理分析,以显著改善AMD诊断.
  • 这项基于Python的研究证明了ML在眼科中的价值,以获得更好的患者结果.