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Diabetic Retinopathy01:27

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

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使用深度学习架构来检测和分类糖尿病视网膜病变.

Cheena Mohanty1, Sakuntala Mahapatra2, Biswaranjan Acharya3

  • 1Department of Electronics and Telecommunication, Biju Patnaik University of Technology, Rourkela 769012, Odisha, India.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

深度学习模型可以准确地检测糖尿病视网膜病变 (DR),这是导致失明的主要原因. 丹森网121的准确率达到了97.30%,优于其他早期DR检测方法.

关键词:
在DenseNet 121中使用.在VGG16中,VGG16是VGG16中的一个.在XGBoost分类器.卷积神经网络是一种卷积神经网络.数据资产负债表数据平衡.糖尿病视网膜病变 糖尿病视网膜病变

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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:

  • 糖尿病视网膜病变 (DR) 是糖尿病的严重并发症,可能导致不可逆转的失明.
  • 早期的DR检测对于及时干预至关重要,但手动对视网膜图像进行分级是低效的,容易出现错误.
  • 需要自动化方法来提高DR诊断的准确性和效率.

研究的目的:

  • 开发和评估深度学习 (DL) 模型,用于自动检测和分类糖尿病视网膜病变 (DR).
  • 为了将混合VGG16-XGBoost网络与DenseNet 121架构的DR分类性能进行比较.
  • 解决视网膜图像数据集中的类不平衡,以改善模型的概括性.

主要方法:

  • 提出了两个DL架构:混合VGG16-XGBoost分类器和DenseNet 121网络.
  • 来自APTOS 2019失明检测卡格尔数据集的视网膜图像被预处理并用于模型培训和评估.
  • 应用了类平衡技术,以减轻不平衡数据分布的影响.

主要成果:

  • 混合VGG16-XGBoost模型在DR分类中实现了79.50%的准确性.
  • 而DenseNet 121模型表现出卓越的性能,准确度为97.30%.
  • 对比分析证实了DenseNet 121对相同数据集的现有方法的增强疗效.

结论:

  • 深度学习架构,特别是DenseNet 121,显示出对糖尿病视网膜病变的准确和高效早期检测和分类的巨大潜力.
  • 自动化DR诊断系统可以提高诊断效率和准确性,有利于患者的治疗结果和医疗保健系统.
  • 丹斯网121模型是眼科临床应用的一个有前途的工具.