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在人眼中识别糖尿病视网膜病变:一种基于计算机辅助诊断系统的混合方法,与深度学习相结合
Şükran Yaman Atcı1, Ali Güneş1, Metin Zontul2
1Department of Computer Engineering, İstanbul Aydın University, Istanbul 34295, Turkey.
概括
深度学习模型现在可以使用眼部扫描检测糖尿病视网膜病变 (DR). 这项研究解决了诸如数据不平衡等挑战,以提高自动化DR检测准确度.
科学领域:
- 生物医学工程 生物医学工程
- 眼科医生 眼科 眼科
- 人工智能的人工智能
背景情况:
- 糖尿病视网膜病变 (DR) 诊断依赖于识别视网膜血管损伤.
- 深度学习 (DL) 模型显示,使用医学眼镜图像进行自动化DR检测是有前途的.
- 诸如不平衡的数据集和注释错误等挑战阻碍了DL模型的性能.
研究的目的:
- 为了比较最先进的深度学习方法来检测糖尿病视网膜病变.
- 评估DR数据集中解决类不平衡的方法.
- 为自动化DR检测确定最佳的混合建模策略.
主要方法:
- 用了三个基准数据集来分析糖尿病视网膜病变.
- 对比了各种最先进的深度学习技术.
- 分析了混合建模,包括卷积神经网络 (CNN) 和SHAP (SHapley增量扩展) 模型的衍生.
主要成果:
- 在不同糖尿病视网膜病变严重程度的高精度得分:93% (正常),89% (轻度),81% (中度),76% (严重) 和96% (DR阶段).
- 证明了混合建模策略在改善DR检测方面的有效性.
- 确定了具体的混合方法,以减轻阶级不平衡的影响.
结论:
- 混合深度学习模型,结合CNN和SHAP,为自动化糖尿病视网膜病变检测提供了强大的解决方案.
- 解决阶级不平衡对于提高DL模型在DR查中的性能至关重要.
- 这项研究提供了对DR诊断中临床应用的理想混合模型的见解.
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