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综合非线性转换用于糖尿病视网膜病变分级

Farida Mohsen¹, Samir Belhaouari¹, Zubair Shah²

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Scientific reports

|August 21, 2025

概括

这项研究引入了RadFuse,一个用于检测和分类糖尿病视网膜病变的新深度学习框架. RadFuse通过将底部图像与新的阴影图表相结合,显著提高了准确性,超过了现有的方法.

科学领域:

眼科眼科
医学成像
人工智能

背景情况:

糖尿病视网膜病变是导致视力丧失的主要原因,
目前的深度学习模型难以理解视网膜图像中的复杂病变模式.
准确的分类对于及时干预和预防失明至关重要.

研究的目的:

推出RadFuse,一个用于增强糖尿病视网膜病变检测和分类的多代表性深度学习框架.
为了更好地捕捉复杂的视网膜病变特征
利用空间和转换域信息进行更好的分类.

主要方法:

开发了RadEx转换,这是Radon转换的非线性延伸,用于创建阴影图表示.
在深度学习框架 (RadFuse) 中与传统的底部图像集成的阴影图像.
在APTOS-2019和DDR数据集上使用ResNeXt-50,MobileNetV2和VGG19进行了RadFuse的评估.

主要成果:

在所有测试的CNN架构中,RadFuse显著改善了糖尿病视网膜病变的检测和分级.
在五级重度分类中达到93.24%的卡帕,在二元分类中达到99.09%的准确性.
在基准数据集上超越了最先进的方法,证明了优越的特征捕获.

结论:

RadFuse有效捕捉复杂的非线性特征,进步糖尿病视网膜病变的分类.
该框架展示了将先进的数学转换纳入医学图像分析的潜力.
这种方法为改善糖尿病视网膜病变的自动查和诊断提供了有希望的方向.

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