基于增强的杆转移学习用于多类光学连贯断层扫描图像分类的图像分类.
Yawar Abbas1, Hassan Jalil Hadi2, Kamran Aziz3
1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China. abbasyawar@whu.edu.cn.
Scientific reports
|February 20, 2025
概括
这项研究引入了一种新的基于强化学习的杆转移学习 (RBLTL) 框架,用于准确诊断视网膜疾病,如糖尿病黄斑 (DME) 和与年龄有关的黄斑退化 (AMD),使用光学连贯断层扫描 (OCT) 图像.
科学领域:
- 眼科和医学成像学
背景情况:
- 准确诊断视网膜疾病,如糖尿病黄斑胀 (DME) 和与年龄相关的黄斑退化 (AMD),对于预防视力丧失至关重要.
- 光学一致性断层扫描 (OCT) 对于识别这些疾病至关重要,特别是AMD,因为其患病率越来越高.
研究的目的:
- 引入一个新的基于强化学习的杆转移学习 (RBLTL) 框架,以加强海外国家和地区的图像分类.
- 提高视网膜疾病自动诊断的准确性和通用性.
主要方法:
- 通过使用预训练模型 (InceptionV3,DenseNet201,InceptionResNetV2) 整合强化学习与转移学习.
- 在RBLTL框架内进行动态超参数优化,以减轻过度拟合和提高性能.
主要成果:
- 在三种场景中,在多类OCT图像分类中实现了98.75%,98.90%和99.20%的高测试准确度.
- 证明了框架在对DME和AMD的OCT图像进行分类方面的有效性.
结论:
- RBLTL框架为自动化医疗图像分类提供了一种可靠和通用的方法.
- 这种方法对改善视网膜疾病的临床诊断具有重大意义.
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