通过灰狼优化的U-Net++和囊网络进行先进的青光眼疾病细分和分类
I Govindharaj1, W Deva Priya2, K L S Soujanya3
1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India. govindharaji@veltech.edu.in.
International ophthalmology
|June 27, 2025
概括
这项研究引入了使用U-Net++和CapsNet的自动化玻璃眼诊断系统,实现了95.1%的早期检测准确度. 这种先进的工具提高了诊断速度和精度,有助于维护视力.
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:
- 玻璃眼是全球不可逆转的失明的主要原因.
- 目前的查方法耗时,需要专家解释,延迟诊断和干预.
- 早期检测对于保证格劳科马患者的视力至关重要.
研究的目的:
- 开发一种自动化眼诊断系统,将优化的U-Net++细分模型与囊网络 (CapsNet) 分类器集成在一起.
- 使用灰狼优化算法 (GWOA) 增强光盘和杯区域的细分.
- 为了从视网膜底部图像中获得准确的玻璃眼的分类.
主要方法:
- 提出了一个两相计算机辅助诊断 (CAD) 框架.
- 通过GWOA优化的增强U-Net++模型被用于对光盘和杯区域进行细分.
- 使用CapsNet架构将图像分类为光眼或正常.
主要成果:
- GWOA-UNet++和CapsNet框架在细分和分类方面实现了95.1%的准确性.
- 该系统在准确性,灵敏性,特异性,精度和F1分数方面超过了现有的基准模型.
- 该模型证明了对图像质量变化和光盘尺寸差异的稳定性.
结论:
- 该自动化系统为早期眼瘤检测提供了增强的诊断准确性,效率和可靠性.
- 这个工具可以作为眼科医生有价值的临床决策支持.
- 未来的工作包括对各种数据集的验证和集成到临床工作流程中,以实现可扩展的部署.
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