期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 116, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2019.103537
关键词
Retinal images; Deep learning; Diabetic retinopathy; Convolutional neural networks
类别
资金
- FAPES [T0041/2016, T0093/2017]
- CNPq
- STIC AMSUD program [88881.117609/2016-01]
- Prodif-Ifes
Detecting the early signs of diabetic retinopathy (DR) is essential, as timely treatment might reduce or even prevent vision loss. Moreover, automatically localizing the regions of the retinal image that might contain lesions can favorably assist specialists in the task of detection. In this study, we designed a lesion localization model using a deep network patch-based approach. Our goal was to reduce the complexity of the model while improving its performance. For this purpose, we designed an efficient procedure (including two convolutional neural network models) for selecting the training patches, such that the challenging examples would be given special attention during the training process. Using the labeling of the region, a DR decision can be given to the initial image, without the need for special training. The model is trained on the Standard Diabetic Retinopathy Database, Calibration Level 1 (DIARETDB1) database and is tested on several databases (including Messidor) without any further adaptation. It reaches an area under the receiver operating characteristic curve of 0.912 - 95%C/(0.897 - 0.928) for DR screening, and a sensitivity of 0.940 - 95%CI(0.921 - 0.959). These values are competitive with other state-of-the-art approaches.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据