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通过血管光学盘交叉细分和注意力特征选择对视网膜图像进行分类

Mahapara Khurshid¹, Chiranjeev Chiranjeev¹, Richa Singh¹

¹Department of Computer Science and Engineering, Indian Institute of Technology, Jodhpur, India.

Scientific reports

|January 16, 2026

概括

这项研究引入了一种人工智能方法,用于分类视网膜图像以检测玻璃眼和糖尿病视网膜病变. 这种新的方法提高了这些严重的眼睛疾病的诊断准确度.

科学领域:

眼科医生眼科眼科
计算机科学计算机科学
医疗成像医学成像

背景情况:

视网膜疾病正在增加,导致失明.
人工智能 (AI) 提供了自动视网膜图像分析的潜力.
挑战包括图像变化和不平衡的数据,阻碍了稳健的模型开发.

研究的目的:

开发一种新的AI方法,用于将视网膜图像分类为健康,青光眼和糖尿病视网膜病变类别.
通过专注于歧视性特征提取和整合多视图信息来提高分类性能.
为了应对诸如低级别之间的差异性和不平衡的数据集等挑战.

主要方法:

采用了一种异常意识的注意力特征选择方法.
一个交叉细分框架被用来提取和整合光盘和血管结构.
该方法在多个公共数据集 (FIVES,Drishti-GS1,SUSTech,HRF,PAPILA) 上得到了验证.

主要成果:

该模型实现了 83% 的平衡精度 (95% CI: 80%-86%).
报告了高灵敏度,特异性,PPV和NPV的健康,玻璃眼和糖尿病视网膜病变分类.
该方法在不同数据集中区分这三个类别方面表现出了强度.

结论:

拟议的AI方法有效地将视网膜图像分类为对玻璃眼和糖尿病视网膜病变的检测.
整合光盘和血管结构可以提高分类的准确性和稳定性.
这种方法对关键视网膜疾病的自动查充满希望.

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