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一个新的卷积神经网络,基于循环和波纹的组合,用于斑点的OCT分类
Roya Arian1,2, Alireza Vard1, Rahele Kafieh3
1Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, 81746-73461, Iran.
Scientific reports
|December 19, 2023
概括
本研究介绍了CircWave,这是一种结合2D-DWT和圆形变换的新型变换,用于改进人工智能 (AI) 对视网膜光连贯断层扫描 (OCT) 图像的分析,以早期检测眼部异常. CircWaveNet可以提高正常和异常病例的诊断准确度.
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:
- 人工智能 (AI) 算法,包括机器学习和深度学习,在分析视网膜光学连贯性断层扫描 (OCT) 图像以早期检测眼部异常方面表现有前途.
- 医疗人工智能的一个重大局限是数据稀缺,这可能会阻碍诊断的准确性.
- 时间频率转换为提高医疗图像分析中的AI性能提供了一个潜在的解决方案.
研究的目的:
- 调查非数据适应性时间频率转换 (X-lets) 对OCT B扫描分类的影响.
- 开发一种新的转换方法,提高正常和异常视网膜OCT图像的同时分类精度.
- 提高眼科人工智能模型的解释性.
主要方法:
- 采用各种X-lets来单独转换OCT B扫描,使用所有得到的子频段作为2D卷积神经网络 (CNN) 的输入.
- 设计了一种新的CircWave变换,通过连接2D离散波形变换 (2D-DWT) 和圆形变换的子频段.
- 评估了来自不同成像系统的两个独立数据集的分类性能.
主要成果:
- 2D-DWT在分类正常病例方面表现出色,而圆形转换在具有循环流体积累的异常病例中表现出色.
- 拟议的CircWave转换在正常和异常情况下显著优于单个转换和原始图像的每类精度.
- 格拉德-CAM可视化表明,CircWaveNet专注于异常情况下的相关圆形形成和正常情况下的线性结构,与原始B扫描不同.
- 在两个不同的数据集上实现了94.5%和90%的高精度,证明了可概括性.
结论:
- 集成到CircWaveNet CNN架构中的CircWave转换提供了通过OCT检测到的眼部异常的卓越诊断准确性.
- 这种方法通过专注于诊断相关特征,提高了人工智能模型的可解释性.
- 环波转换代表了人工智能辅助眼科诊断的重大进步,解决了数据限制并提高了分类性能.
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