Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume 108, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.artmed.2020.101936
Keywords
Diabetic retinopathy grading; Coarse-to-fine classification; Convolutional neural networks; Fundus images
Funding
- State's Key Project of Research and Development Plan [2017YFA0104302, 2017YFC0109202, 2017YFC0107900]
- National Natural Science Foundation [61801003, 61871117, 81471752]
- China Scholarship Council [201906090145]
Ask authors/readers for more resources
Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity grades using convolutional neural networks (CNNs). The CF-DRNet conforms to the hierarchical characteristic of DR grading and effectively improves the classification performance of five-class DR grading, which consists of the following: (1) The Coarse Network performs two-class classification including No DR and DR, where the attention gate module highlights the salient lesion features and suppresses irrelevant background information. (2) The Fine Network is proposed to classify four stages of DR severity grades of the grade DR from the Coarse Network including mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR (PDR). Experimental results show that proposed CF-DRNet out-performs some state-of-art methods in the publicly available IDRiD and Kaggle fundus image datasets. These results indicate our method enables an efficient and reliable DR grading diagnosis in clinic.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available