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结合深度学习和EfficientNet,准确诊断糖尿病视网膜病变

Lakshay Arora¹, Sunil K Singh², Sudhakar Kumar¹

¹Department of CSE, Chandigarh College of Engineering and Technology, Panjab University, Chandigarh, India.

Scientific reports

|December 18, 2024

概括

本研究引入了一个使用EfficientNetB0的深度学习框架,用于从视网膜图像中诊断糖尿病视网膜病变 (DR). 该模型实现了86.53%的准确性,为早期DR检测提供了一个有前途的工具,并改善了患者的治疗结果.

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:

糖尿病视网膜病变 (DR) 是全球视力损失的主要原因.
早期发现DR对于有效治疗和维护视力至关重要.

研究的目的:

开发和评估用于自动化DR诊断的深度学习框架.
使用视网膜图像对DR严重程度进行分类.

主要方法:

使用EfficientNetB0架构进行图像分类.
采用先进的卷积神经网络 (CNN) 分层技术.
在"糖尿病视网膜病症诊断"数据集 (35108张图像) 中接受培训和测试.

主要成果:

实现了平均准确率为86.53%.
记录了0.5663.3的损失率.
在DR重症级别中表现出高准确性和概括性.

结论:

EfficientNetB0框架显示了对准确可靠的DR诊断的巨大潜力.
这种工具可以帮助临床医生进行早期干预,有可能改变DR诊断和治疗.
使用更大的数据集和资源进行进一步开发可以提高临床效用.

关键词:

在美国,CNN是CNN.深度学习是一种深度学习.糖尿病视网膜病变 - 糖尿病视网膜病变有效的网络有效的网络图像数据集是一组图像数据集.层层地进行分层.

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