Name
Title | ||
|---|---|---|
An Efficient Deep Learning Network for Automatic Detection of Neovascularization in Color Fundus Images |
Abstract | ||
|---|---|---|
Retinopathy screening is a non-invasive method to collect retinal images and neovascularization detection from retinal images plays a significant role on the identification and classification of diabetes retinopathy. In this paper, an efficient deep learning network for automatic detection of neovascularization in color fundus images is proposed. The network employs Feature Pyramid Network and Vovnet as the backbone to detect neovascularization. The network is evaluated with color fundus images from practice. Experimental results show the network has less training and test time than Mask R-CNN while with a high accuracy of 98.6%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EMBC46164.2021.9629572 | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) |
DocType | Volume | ISSN |
Conference | 2021 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||