使用HHO-CBL模型进行糖尿病视网膜病变预诊断的优化框架
Bratislavske lekarske listy
|February 22, 2024
概括
早期发现糖尿病视网膜病变 (DR) 对于预防视力丧失至关重要. 一个新的哈里斯霍克优化与CNN-Bi-LSTM (HHO-CBL) 模型准确诊断DR及其严重程度,优于现有方法.
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:
- 糖尿病视网膜病变 (DR) 是糖尿病的严重并发症,影响视网膜,并可能导致视力丧失.
- 早期发现DR对于预防不可逆转的视力损伤至关重要,但传统方法难以识别微动脉瘤等早期指标.
研究的目的:
- 开发一种先进的模型,用于早期检测和准确分类糖尿病视网膜病变.
- 确定微动脉瘤形成的主要原因,对于DR预诊断至关重要.
主要方法:
- 使用CNN-Bi-LSTM (HHO-CBL) 模型实现一种新的哈里斯·霍克优化,用于特征提取.
- 使用HHO-CBL模型检测微动脉瘤指标,如囊扩张和血管曲.
- 在Kaggle APTOS数据集上对HHO-CBL模型的训练和验证,用于DR严重程度分类.
主要成果:
- 该HHO-CBL模型在早期DR诊断方面实现了96.4%的准确性.
- 该模型在将DR分为五个严重程度等级时显示了98.8%的准确性.
- 对比分析证实了该模型的性能优于DR预诊断和分期的现有方法.
结论:
- 开发的HHO-CBL模型有效地识别了微动脉瘤前体,并诊断了所有五种级别的DR.
- 在HHO-CBL模型中使用的特征嵌入技术在早期DR诊断和多级分类中表现出卓越的性能.
- 这种创新方法为改善DR检测和管理提供了一个有希望的解决方案.
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