使用U形和囊网络的灰狼优化技术 - - 一个用于青光眼诊断的新框架
Govindharaj I1, Ramesh T2, Poongodai A3
1Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, 600062, India.
MethodsX
|April 16, 2025
概括
一个用于眼查的自动化系统结合了先进的细分和分类. 这种人工智能驱动的方法实现了96.01%的准确性,改善了对青光眼的早期检测,以预防失明.
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:
- 玻璃眼是全球不可逆转失明的主要原因.
- 当前的诊断方法往往耗时,需要专门的专业知识.
- 早期检测至关重要,以防止显著的视力丧失.
研究的目的:
- 开发一个自动化的青光眼查平台.
- 为了提高青光眼诊断的准确性和效率.
- 为了克服手动选过程的局限性.
主要方法:
- 使用灰狼优化算法 (GWOA) 和U-Net++进行光盘提取的混合细分技术.
- 一个深度学习分类模型,CapsNet,用于强大的青光眼的检测.
- 将细分和分类集成为一个全面的诊断系统.
主要成果:
- 在组合的细分和分类中获得了96.01%的准确性.
- 与传统的玻璃眼查方法相比,表现优越.
- 突出了该系统在早期发现青光眼的能力.
结论:
- 该自动化系统显著提高了对绿眼病诊断的临床准确性.
- 这种人工智能驱动的方法提供了一个更有效和更容易获得的查解决方案.
- 开发的框架显示了减少全球眼引起的失明的巨大潜力.
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