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使用深度学习模型进行显微镜辅助高血压视网膜病变诊断.

Shahzad Akbar¹, Usama Shahzore¹, Tanzila Saba²

¹Riphah Artificial Intelligence Research (RAIR) Lab, Riphah College of Computing, Riphah International University, Faisalabad, Pakistan.

Microscopy research and technique

|March 25, 2025

概括

本研究引入了一种使用U-Net和Dense-Net的自动化方法,用于从视网膜图像中检测和分级高血压视网膜病变 (HR). 这种新的技术实现了高精度,有助于及时临床诊断和预防失明.

关键词:

动脉静脉比率的比率是什么动脉/静脉分类的分类方法危害健康的风险高血压视网膜病变这是一种高血压视网膜病变.显微镜辅助成像技术使用失去了视觉,失去了视觉.

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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:

高血压视网膜病变 (HR) 是影响视网膜的高血压并发症,可能导致失明.
从视网膜图像中手动诊断HR是劳动密集型的,容易产生不准确性.
早期发现和分类HR对于有效的患者管理和预防视力丧失至关重要.

研究的目的:

利用深度学习模型开发和评估一种新的,自动化的技术,用于检测和分级高血压视网膜病变 (HR).
与手动方法相比,提高HR诊断的准确性和效率.
提供一种用于临床应用的工具,用于识别和分类HR阶段.

主要方法:

一种结合U-Net和Dense-Net架构的新方法,用于自动化的人力资源检测和分级.
使用Gabor过器进行图像预处理,以增强视网膜血管结构.
使用U-Net进行血管细分,然后使用Dense-Net进行动脉/静脉分类.
从HR评估的分类血管计算动脉静脉比率 (AVR).

主要成果:

自动化系统在AVRDB数据集上实现了高性能.
人力资源分类的平均准确率为99.40%.
人力资源分级的平均准确率达到99.77%.

结论:

拟议的基于U-Net和Dense-Net的方法提供了一个高度准确和高效的解决方案,用于自动化HR检测和分级.
这种自动化方法可以通过提供可靠的诊断支持,显著有利于临床实践.
该技术具有早期干预的潜力,并改善了高血压视网膜病变的管理,减少了失明的风险.