论文标题
通过卷积神经网络在视网膜眼底图像中早期发现早产（ROP）的早期检测
Early Detection of Retinopathy of Prematurity (ROP) in Retinal Fundus Images Via Convolutional Neural Networks
论文作者
论文摘要
早产性视网膜病变（ROP）是过早出生的婴儿或出生体重低的婴儿的视网膜异常的血管发育。 ROP是全球婴儿失明的主要原因之一。 ROP的早期检测对于降低和避免ROP引起的视力障碍的进展至关重要。然而，即使是医疗专业人员，对ROP的认识也有限。因此，如果有的话，ROP的数据集受到限制，并且在负面图像和正面图像之间的比率方面通常会极度失衡。在这项研究中，我们提出了在优化框架中检测视网膜底面图像中ROP的问题，并应用最先进的卷积神经网络技术来解决此问题。基于我们模型的实验结果达到100％的敏感性，96％的特异性，98％的精度和96％的精度。此外，我们的研究表明，随着网络变得更深，可以提取更重要的功能，以更好地理解ROP。
Retinopathy of prematurity (ROP) is an abnormal blood vessel development in the retina of a prematurely-born infant or an infant with low birth weight. ROP is one of the leading causes for infant blindness globally. Early detection of ROP is critical to slow down and avert the progression to vision impairment caused by ROP. Yet there is limited awareness of ROP even among medical professionals. Consequently, dataset for ROP is limited if ever available, and is in general extremely imbalanced in terms of the ratio between negative images and positive ones. In this study, we formulate the problem of detecting ROP in retinal fundus images in an optimization framework, and apply state-of-art convolutional neural network techniques to solve this problem. Experimental results based on our models achieve 100 percent sensitivity, 96 percent specificity, 98 percent accuracy, and 96 percent precision. In addition, our study shows that as the network gets deeper, more significant features can be extracted for better understanding of ROP.
