HDL-ACO混合深度学习和殖民地优化用于眼睛光学连贯性断层扫描图像分类
Shivani Agarwal1, Anand Kumar Dohare2, Pranshu Saxena3
1Department of Information Technology, Ajay Kumar Garg Engineering College, Ghaziabad, India.
Scientific reports
|February 18, 2025
概括
本研究介绍了HDL-ACO,这是一种混合深度学习模型,可以提高光学连贯断层扫描 (OCT) 图像分类的准确性和效率. 新的框架增强了眼部疾病的诊断能力.
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:
- 光学连贯断层扫描 (OCT) 对于诊断眼睛疾病至关重要.
- 传统的卷积神经网络 (CNN) 在海外国家和地区的分析中扎着计算负载,噪音和不平衡的数据.
研究的目的:
- 引入HDL-ACO,这是一个混合深度学习 (HDL) 框架,集成CNN和殖民地优化 (ACO).
- 为了提高OCT图像分析的分类准确性和计算效率.
主要方法:
- 通过离散波波变换和ACO优化的增强预处理OCT数据.
- 使用多尺度补丁嵌入和ACO进行超参数优化.
- 采用基于变压器的特征提取模块,具有内容意识嵌入和多头自我注意.
主要成果:
- HDL-ACO实现了95%的训练准确率和93%的验证准确率.
- 性能优于像ResNet-50,VGG-16和XGBoost这样的最先进的模型.
- 证明了功能选择和培训效率的改善.
结论:
- HDL-ACO提供了一个可扩展和资源高效的解决方案,用于实时的OCT图像分类.
- 该框架显著提高了眼科诊断能力.
- 解决了医疗图像分析中传统CNN模型的局限性.
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