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OPU-NET-DADENA:优化深度学习组合与u-net细分,用于早期检测糖尿病视网膜病变

N Kavitha¹, S Sridevi¹

¹Department of Computer Science and Engineering, Vels Institute of science, Technology and Advanced Studies (VISTAS), Krishnapuram, Pallavaram, Chennai, Tamil Nadu, 600117, India.

Microvascular research

|February 20, 2026

概括

糖尿病视网膜病变 (DR),糖尿病 (DM) 的并发症,可以导致失明. 本研究引入了用于早期DR检测的深度学习模型,实现高精度和精度,以防止视力丧失.

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 (DM) 的主要微血管并发症,在早期往往无症状.
视网膜损伤视网膜血管,如果不及早发现,可能导致不可逆转的失明.
有效和准确的早期诊断系统对于预防糖尿病患者视力丧失至关重要.

研究的目的:

为早期检测糖尿病视网膜病变 (DR) 提出一种新的深度学习方法.
开发一个高效的系统,能够在DR的初始阶段识别DR,从而防止严重的视力障碍.

主要方法:

一种深度学习方法,利用Deep Siamese DenseNet进行DR检测.
图像预处理与对比度有限的自适应直方体平衡 (CLAHE) 和使用优化U-NET进行细分.
通过剩余注意力EfficientNet (RA-EfficientNet) 提取特征,并使用爬行动物搜索算法 (RORS) 进行选择.
最终检测使用DarkNet,DenseNet 201和NasNetMobile. 最终检测使用DarkNet,DenseNet 201和NasNetMobile. 最终检测使用DarkNet,DenseNet 201和NasNetMobile. 最终检测使用DarkNet.

主要成果:

提出的深度学习模型的准确率达到99.33%,精度达到98.32%.
这些结果明显优于以前的方法,报告准确度为95-97%,精度为94-96%.
该模型在各种评估指标上显示出高性能,包括F-score,特异性和灵敏性.

结论:

开发的深度学习方法提供了一个高度有效和可靠的解决方案,用于早期检测糖尿病视网膜病变.
这种先进的系统有可能通过及时干预和预防失明,显著改善患者的治疗结果.
这项研究强调了将先进的深度学习技术整合到诊断诸如DR等复杂眼病的疗效.

关键词:

深度学习是一种深度学习.糖尿病视网膜病变 - 糖尿病视网膜病变微血管并发症微血管并发症视网膜血管中的血管.视力问题视力问题

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