使用深度学习的OCT图像自动检测年龄相关性黄斑变性和糖尿病性黄斑水肿

S. Kaymak, Ali Serener
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引用次数: 49

摘要

老年性黄斑变性(AMD)是一种眼部疾病,损害视网膜,导致视力丧失。糖尿病性黄斑水肿(DME)也是糖尿病患者视力丧失的一种形式。因此,早期发现AMD和DME对于及时治疗眼睛和预防任何视力损害至关重要。提出了在光学相干断层扫描(OCT)图像上自动检测DME和AMD的方法。使用的方法是基于训练深度学习算法将它们分为健康、干性AMD、湿性AMD和DME类别。该方法优于最近在文献中提出的基于迁移学习的方法,用于将OCT图像分类为AMD和DME类别。
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Automated Age-Related Macular Degeneration and Diabetic Macular Edema Detection on OCT Images using Deep Learning
Age-related macular degeneration (AMD) is an eye disease that damages the retina, causing vision loss. Diabetic macular edema (DME) is also a form of vision loss for diabetic people. It is therefore crucial to detect AMD and DME in the early stages for the timely treatment of the eye and the prevention of any vision impairment. Automatic detection of DME and AMD on optical coherence tomography (OCT) images are presented in this paper. The method used is based on training a deep learning algorithm to classify them into healthy, dry AMD, wet AMD and DME categories. This method outperforms a transfer learning based method proposed recently in the literature for classification of OCT images into AMD and DME categories.
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