使用混合深度学习和殖民地优化进行光学一致性断层图像分类
Awais Khan1, Kuntha Pin1, Ahsan Aziz1
1Department of ICT Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种深度学习方法,用于使用光学连贯断层扫描 (OCT) 图像准确检测视网膜疾病. 自动化系统实现了99.1%的准确性,大大改善了手动诊断.
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:
- 从光学一致性断层扫描 (OCT) 图像中手动检测视网膜疾病是主观的,容易出错.
- 现有的自动化方法需要进一步提高准确性,以便可靠的早期检测.
- 深度学习方法显示出增强自动视网膜疾病诊断的潜力.
研究的目的:
- 开发和评估一种基于深度学习的自动化方法,用于使用OCT图像检测和分类多种视网膜疾病.
- 提高视网膜疾病自动诊断的准确性和可靠性.
- 为了比较拟议的方法的性能与没有特征优化技术.
主要方法:
- 使用了三种预训练的深度学习模型 (DenseNet-201,InceptionV3,ResNet-50) 通过转移学习进行特征提取.
- 使用殖民地优化 (ACO) 来增强提取的功能并选择最相关的功能.
- 使用K-近邻 (KNN) 和支持矢量机 (SVM) 算法与优化功能的视网膜疾病分类.
主要成果:
- 拟议的深度学习方法在结合群优化 (ACO) 时实现了99.1%的高精度.
- 没有ACO,该方法的准确性达到97.4%.
- 该系统展示了最先进的性能,在视网膜疾病分类的准确性方面超过了现有的技术.
结论:
- 开发的深度学习模型有效地检测和分类来自OCT图像的视网膜疾病.
- 殖民地优化显著提高了自动诊断系统的性能.
- 这种自动化方法为手动诊断提供了可靠和准确的替代方案,有助于早期检测糖尿病黄斑胀和与年龄相关的黄斑变性等疾病.
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