对糖尿病黄斑 edem 的深度和机器学习的优化策略的审查
A M Mutawa1, Khalid Sabti2, Bibin Shalini Sundaram Thankaleela1
1Department of Computer Engineering, College of Engineering and Petroleum, Kuwait University, Sabah Al Salem University City, Shadadiya, Kuwait.
Frontiers in artificial intelligence
|March 2, 2026
概括
优化算法显著提高了糖尿病黄斑 (DME) 分析的深度学习 (DL) 和机器学习 (ML) 模型. 使用元启发式优化器的混合模型在分级糖尿病视网膜病变和DME时显示出高准确度.
科学领域:
- 眼科图像分析的方法
- 计算机科学和工程 计算机科学和工程
- 医疗保健中的人工智能
背景情况:
- 糖尿病黄斑 (DME) 是糖尿病患者视力丧失的主要原因.
- 准确及时分析DME对于有效治疗至关重要.
- 深度学习 (DL) 和机器学习 (ML) 在眼科图像分析中表现有前途,但优化通常被忽视.
研究的目的:
- 审查和整合关于DL和ML应用DME的现有研究.
- 强调优化算法的对模型有效性,稳定性和性能的影响.
- 评估AI在计算机科学和工程领域的DME分析中的当前状态.
主要方法:
- 使用人口,干预,比较和结果 (PICO) 框架进行系统审查.
- 使用乔安娜·布里格斯研究所工具对包括的研究进行批判性评估.
- 优化策略的分析,包括像Jaya和群优化等元启发式算法.
主要成果:
- 混合DL/ML架构,特别是Auto-Metric Graph神经网络,在增强优化器时,在结合糖尿病视网膜病变-DME分级方面实现了高精度 (99.57%).
- 像Jaya和殖民地优化等元启发式优化器可以提高模型性能.
- 仍然存在挑战,包括基于YOLO的模型的病变识别中低平均精度 (0.1540) 和需要改进临床解释性.
结论:
- 优化算法对于提高DL和ML模型在DME分析中的性能至关重要.
- 混合架构与先进的优化技术相结合,提供了卓越的准确性.
- 需要进一步的研究来解决目前的局限性,并加强对DME的AI的临床采用.
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