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RDS-DR:一种改进的深度学习模型,用于对糖尿病视网膜病变的严重程度进行分类

Ijaz Bashir¹, Muhammad Zaheer Sajid¹, Rizwana Kalsoom²

¹Department of Computer Software Engineering, Military College of Signals, National University of Sciences and Technology, Islamabad 44000, Pakistan.

Diagnostics (Basel, Switzerland)

|October 14, 2023

概括

这项研究引入了一种新的AI模型,用于早期糖尿病视网膜病变 (DR) 检测,使用 fundus 图像. 剩余密度系统 (RDS-DR) 达到97.5%的准确性,帮助医疗保健专业人员诊断DR.

科学领域:

眼科和人工智能的人工智能

背景情况:

糖尿病视网膜病变 (DR) 是一种与高血糖相关的眼睛疾病,经常出现晚期症状.
严重的DR可以导致视力丧失,视神经损伤和视网膜动脉阻塞.

研究的目的:

开发一种由人工智能驱动的自动化方法,用于早期诊断糖尿病视网膜病变.
创建一个新的深度学习模型,使用数字基金图像进行准确的DR分类.

主要方法:

一个名为"DR-Insight"的新数据集是从巴基斯坦眼科医院和在线来源创建的.
通过将残余和密集块集成到深度神经网络中,开发了一个残余密集系统 (RDS-DR) 模型.
在RDS-DR模型的训练中,使用了9860个 fundus 图像.

主要成果:

该RDS-DR模型在分类糖尿病视网膜病变时实现了97.5%的准确性.
拟议的模型在精度方面超过了VGG19,VGG16,Inception V-3和Xception.
该系统证明了其作为医疗保健从业人员的诊断辅助工具的有效性.

结论:

RDS-DR系统显示出作为早期糖尿病视网膜病变检测的诊断工具的巨大潜力.
这种人工智能模型可以增强,而不是取代眼科医生的专业知识.
该研究强调了新的RDS-DR方法对DR分类的成功.

关键词:

深度学习是一种深度学习.深度神经网络是一个神经网络.密集区块是一个密集的区块.糖尿病视网膜病变糖尿病视网膜病变剩余区块是一个残留区块.支持矢量机器的支持矢量机器.

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