使用U-Net和U-Net+架构使用深度学习技术进行增强的青光眼检测
B P Pradeep Kumar1, Pramod K B Rangaiah2, Robin Augustine2
1Department of Computer Science and Design, Atria Institute of Technology, Bengaluru 560024, India.
Photodiagnosis and photodynamic therapy
|June 7, 2025
概括
这项研究通过先进的深度学习和图像处理来增强青光眼的诊断. 新方法显著提高了检测疾病的准确性和可靠性.
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:
- 青光眼的诊断严重依赖于精确的光盘和视网膜神经纤维层分析.
- 当前的诊断方法可能受到图像噪声和细分挑战的限制.
- 深度学习为改进绿眼病自动检测提供了潜在的潜力.
研究的目的:
- 开发和评估使用图像处理和深度学习来诊断眼的增强方法.
- 为了比较U-Net和U-Net+在光盘细分方面的有效性.
- 评估拟议的综合模型的整体诊断性能.
主要方法:
- 使用中间波器减少图像噪声.
- 使用U-Net和U-Net+架构进行光盘细分.
- 使用囊网络提取特征,并通过极端学习机器 (ELM) 进行分类.
主要成果:
- 过的中位数实现了97.88%的噪声降低 (PSNR: 44.99).
- 在细分方面,U-Net的表现优于U-Net+ (迪斯:0.8557,贾卡德:0.7307).
- 综合模型的诊断准确性很高:99% (DRISHTI-GS),99.5% (DRIONS-DB),98.5% (HRF). 综合模型的诊断准确性很高,包括99% (DRISHTI-GS),99.5% (DRIONS-DB) 和98.5% (HRF).
结论:
- 提出的深度学习方法显著提高了青光眼诊断的准确性.
- 中位过和U-Net细分是有效的预处理步骤.
- 这种方法有望改善患者在绿眼管理中的结果.
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