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一个深度学习框架,用于早期检测多视网膜疾病

Sara Ejaz¹, Raheel Baig², Zeeshan Ashraf²

¹Department of Information and Technology, University of Gujrat, Gujrat, Punjab, Pakistan.

|July 25, 2024

概括

深度学习模型,特别是卷积神经网络 (CNN),显示出使用视网膜底图像早期检测眼睛疾病的前景. 这项研究在识别糖尿病视网膜病变等疾病方面取得了很高的准确性.

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:

视网膜底部图像对于诊断眼部疾病至关重要.
眼部疾病的早期检测和治疗通过快速图像分析来促进.
深度学习算法提供了从视网膜图像自动诊断的潜力.

研究的目的:

开发一种使用卷积神经网络 (CNN) 早期检测和治疗多种眼病的非侵入性方法.
评估不同CNN模型在分类视网膜底图像中的性能,这些图像代表了各种眼部疾病和健康的眼睛.

主要方法:

使用了视网膜底部多种疾病图像数据集 (RFMiD),其中包含媒体雾 (MH),光盘缩 (ODC),糖尿病视网膜病变 (DR) 和健康 (WNL) 眼睛的图像.
应用预处理技术,包括数据增强,裁剪,调整大小,数据集分割,图像到数组转换和一次性编码.
用三种CNN模型进行实验,提取用于预测诊断决策的特征,并使用准确度,F1得分,回忆和精度评估性能.

主要成果:

一个12层CNN通过数据增强实现了高达89.81%的验证率和88.72%的测试准确率.
20层CNN通过增强数据达到90.34%的验证率和89.59%的测试准确率,尽管它表现出过度拟合.
取得的准确率表明,CNN模型能够有效地区分不同的眼睛疾病和健康图像.

结论:

开发的深度学习框架证明了可靠和高效的同时检测多种眼病的能力.
CNNs可以准确地分析色底图像,以改善眼科诊断决策.
这种方法支持通过早期和准确识别各种眼部疾病,及时规划治疗.

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