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基于焦色视网膜底部图像的深度学习系统,以协助诊断
Yanli Zou1,2, Yujuan Wang3, Xiangbin Kong2
1School of Basic Medical Sciences, Southern Medical University, Guangzhou 510000, China.
Diagnostics (Basel, Switzerland)
|September 28, 2023
概括
本研究介绍了一种使用卷积神经网络 (CNN) 的智能系统,用于从 fundus 图像 (FI) 中辅助诊断视网膜疾病. 该系统精确检测硬液 (HE),显示出早期糖尿病视网膜病变诊断的高可靠性.
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:
- 视网膜疾病显著影响视力和生活质量,发病率越来越高.
- 传统的诊断依赖于经验丰富的临床医生对 fundus 图像的主观解释.
- 需要自动诊断工具来提高准确性并减少误诊风险.
研究的目的:
- 分析用于焦点视网膜图像辅助诊断的智能医疗系统.
- 利用卷积神经网络 (CNN) 在 fundus 图像 (FI) 中识别,分类和检测硬排泄物 (HE).
主要方法:
- 开发一个集成CNN的智能医疗系统.
- 该系统的应用用于分析用于疾病检测的焦点视网膜图像.
- 在识别和分类硬排泄物 (HE) 中评估系统性能.
主要成果:
- 该系统在诊断色彩视网膜FI的病态变化方面实现了高准确度 (86.4%98.6%).
- 传统视网膜病变FI的表现显示精度在70.1%至85%.
- 该系统在检测硬排泄物 (HEs) 中表现出高精度和回忆.
结论:
- 使用焦点视网膜FI的智能系统在早期检测糖尿病视网膜病变方面表现出高准确性和可靠性.
- 开发的系统具有在眼科诊断中临床应用的巨大潜力.
- 基于CNN对 fundus图像的分析为客观和高效的视网膜疾病诊断提供了一个有希望的方法.
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