早产儿视网膜病变
卷积神经网络
计算机科学
儿童失明
人工智能
深度学习
视网膜病变
眼底(子宫)
人工神经网络
疾病
模式识别(心理学)
医学
眼科
病理
胎龄
内分泌学
糖尿病
生物
怀孕
遗传学
作者
Junjie Hu,Yuanyuan Chen,Jie Zhong,Rong Ju,Yi Zhang
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:38 (1): 269-279
被引量:77
标识
DOI:10.1109/tmi.2018.2863562
摘要
Retinopathy of Prematurity (ROP) is a retinal vasproliferative disorder disease principally observed in infants born prematurely with low birth weight. ROP is an important cause of childhood blindness. Although automatic or semi-automatic diagnosis of ROP has been conducted, most previous studies have focused on “plus” disease, which is indicated by abnormalities of retinal vasculature. Few studies have reported methods for identifying the “stage” of the ROP disease. Deep neural networks have achieved impressive results in many computer vision and medical image analysis problems, raising expectations that it might be a promising tool in the automatic diagnosis of ROP. In this paper, convolutional neural networks with a novel architecture are proposed to recognize the existence and severity of ROP disease per-examination. The severity of ROP is divided into mild and severe cases according to the disease progression. The proposed architecture consists of two sub-networks connected by a feature aggregate operator. The first sub-network is designed to extract high-level features from images of the fundus. These features from different images in an examination are fused by the aggregate operator, then used as the input for the second sub-network to predict its class. A large data set imaged by RetCam 3 is used to train and evaluate the model. The high classification accuracy in the experiment demonstrates the effectiveness of the proposed architecture for recognizing the ROP disease.
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