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血管生物标志物和糖尿病视网膜病变检测深度特征的图形增强多式融合

K V Deepsahith¹, Basineni Shashank¹, Bangipavan Kumar¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in artificial intelligence

|February 18, 2026

概括

这项研究引入了用于糖尿病视网膜病变 (DR) 检测的新型多式模式框架,将深度学习特征与血管生物标志物相结合. 该方法实现了高精度,为早期DR诊断提供了一种可靠的方法.

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) 的检测依赖于视网膜成像分析.
整合深度特征和血管生物标志物可以提高诊断准确度.

研究的目的:

提出一个多式模式框架,将深层视网膜特征和血管生物标志物结合起来,以改善DR检测.
为了利用变压器融合架构来整合各种数据模式.

主要方法:

预处理 fundus 图像使用 CLAHE,Canny 边缘检测,顶帽转换和 U-Net 容器细分.
使用CBAM融合的MobileNetV3骨干提取深层空间特征.
使用碎形维度分析,AVR和GLCM纹理通过GNN生成血管图嵌入.
集成功能使用基于变压器的交叉模式融合与多头交叉注意.

主要成果:

在Messidor-2数据集上获得了93.8%的准确性,92.1%的精度,92.8%的回忆率和0.96的AUC-ROC.
在Eyepacs和APTOS 2019数据集上显示了超过98%的准确性,用于DR检测.
超过了传统的深度学习基线和现有的最先进的方法.

结论:

拟议的多式联络框架为糖尿病视网膜病变检测提供了可靠和准确的方法.
通过变压器融合结合深度学习和血管生物标志物,显著提高了诊断性能.
该系统在自动化DR查和诊断方面是一个有前途的进步.

关键词:

卷积神经网络 (CNN) 是一种神经网络.移动网络V3 移动网络V3 对比有限的适应性历史图平衡 (CLAHE)深度学习是一种深度学习.视网膜图像视网膜图像

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