通过使用深度学习的VAE增强图像对角的计算机辅助诊断
Zhila Agharezaei1,2,3, Reza Firouzi4, Samira Hassanzadeh5
1Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
Scientific reports
|November 23, 2023
概括
深度学习模型使用角膜拓学准确检测角 (KCN). 合成图像提高了性能,为眼科中先进的计算机辅助诊断系统铺平了道路.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 对于眼科医生来说,形眼球 (KCN) 检测是具有挑战性和耗时的.
- 准确的诊断依赖于审查人口统计和临床眼科检查.
- 角膜地形图对于KCN评估至关重要.
研究的目的:
- 开发和评估深层卷积神经网络 (CNN) 模型用于KCN检测.
- 用角膜地形图来评估AI模型的准确性.
- 探索数据增强对KCN检测性能的影响.
主要方法:
- 从1010名受试者中回顾收集了1758张角膜图像.
- 使用变量自动编码器 (VAE) 来创建4000个样本的数据集增强.
- 四个深度学习模型 (VGG16,EfficientNet-B0等) 的培训和评估. 在原始和合成图像上.
主要成果:
- 深度学习模型实现了高精度,从95% (EfficientNet-B0) 到99% (VGG16) 之间.
- 所有模型都表现出超过0.94的灵敏度和特异性;VGG16的AUC达到0.99.
- 合成图像在培训过程中提高了分类性能.
- 一个定制的CNN模型提供了准确性 (0.97) 和速度的平衡.
结论:
- 深度学习模型显示使用角膜拓学进行KCN查的高精度.
- 人工智能驱动的系统可以帮助眼科医生在KCN的临床决策中.
- 这项技术通过增强的计算机辅助诊断 (CAD) 支持快速而精确的KCN治疗.
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