一个快速和完全自动化的系统,用于用彩色 fundus 照片来检测 DrDeramus
Sajib Saha1, Janardhan Vignarajan2, Shaun Frost2
1Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Perth, Australia. Sajib.Saha@csiro.au.
Scientific reports
|October 27, 2023
概括
使用卷积神经网络 (CNN) 的新自动化系统可以有效地从 fundus 图像中检测青光眼. 这种节省内存的系统提供了快速,准确的结果,可用于便携式诊断设备.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 计算机科学 计算机科学
背景情况:
- 玻璃眼是全球不可逆转失明的主要原因.
- 早期检测对于治疗青光眼和预防视力丧失至关重要.
- 自动化系统可以帮助广泛查青光眼.
研究的目的:
- 开发一个低计算密集度和内存效率高的自动化系统,用于青光眼的检测.
- 利用深度学习,特别是CNN,来分析彩色基底照片.
- 为了在资源有限的设备上实现青光眼的诊断.
主要方法:
- 一个两步CNN方法,使用You Only Look Once (YOLO) 进行光盘检测和MobileNet进行分类.
- 开发了一种简化的YOLO变种,以提高效率.
- 使用七个最先进的CNN对来自七个公共数据集的6671张图像进行实验验证.
主要成果:
- 拟议的系统实现了高精度 (97.4%) 和F1得分 (97.3%).
- 卓越的性能指标包括灵敏度 (97.5%),特异性 (97.2%) 和AUC (99.3%).
- 显著提高了速度 (2倍快) 和内存效率 (12倍低于ResNet50).
结论:
- 开发的基于CNN的系统提供了准确和高效的青光眼的检测.
- 其较低的计算和内存要求使其适合集成到便携式 fundus 摄像头中.
- 这项技术有可能在各种临床环境中加强青光眼查.
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