基因灰狼优化和内核极端学习机器的新融合,用于精确的糖尿病眼病分类
Abdul Qadir Khan1, Guangmin Sun1, Majdi Khalid2
1Faculty of Information Technology, Beijing University of Technology, Beijing, China.
PloS one
|May 20, 2024
概括
这项研究引入了用于早期糖尿病眼病 (DED) 检测的先进AI模型. 这种新的方法显著提高了从底部图像中诊断DED阶段的准确性.
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:
- 糖尿病眼病 (DED) 是全球视力损失的主要原因.
- 准确和早期发现DED对于有效的患者管理至关重要.
- 当前的诊断方法可能耗时,需要专门的专业知识.
研究的目的:
- 开发和验证一种新的AI驱动系统,用于糖尿病眼病的自动检测和分类.
- 提高糖尿病视网膜病变分级和疾病类型分化的精度.
- 提高DED查的效率和可访问性.
主要方法:
- 将遗传灰狼优化 (G-GWO) 算法与完全卷积编码解码网络 (FCEDN) 的集成.
- 使用了内核极端学习机器 (KELM) 来进行精细的图像细分和分类.
- 在不同的数据集上评估模型性能:IDRiD,DR-HAGIS和ODIR.
主要成果:
- 在多个数据集中实现了高分类准确度,从98.5%到98.8%不等.
- 与现有最先进的方法相比,表现出优越的性能.
- 成功实现了DED阶段的精确检测和疾病类型的差异化.
结论:
- 拟议的G-GWO-FCEDN-KELM模型代表了自动化基底图像分析在DED的显著进步.
- 这种方法有可能减少对手工检查的依赖,提高诊断效率.
- 这些发现支持提高诊断准确性和改善DED管理中的患者结果.
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