Journal
IEEE ACCESS
Volume 8, Issue -, Pages 38493-38500Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2975745
Keywords
Retina; Biomedical imaging; Blood vessels; Image segmentation; Diseases; Training; Hemorrhaging; Retina vessel segmentation; convolutional neural network; U-Net; residual block; F1-Score
Categories
Funding
- National Council of Reseach of Mexico (CONACyT)
Ask authors/readers for more resources
Retina images are the only non-invasive way of accessing the cardiovascular system, offering us a means of observing patterns such as microaneurysms, hemorrhages and the vasculature structure which can be used to diagnose a variety of diseases. The main goal of this paper is to automate retinal blood vessel segmentation with a good tradeoff between blood vessel classification and training time in the presence of high unbalanced classes. In this work, a novel methodology is proposed using two convolutional neural networks (CNN& x2019;s), chained to each other. The second CNN has been designed with residual network blocks, which joined to the information flow from the first, give us metrics like recall and F1-Score, which are, in most cases, superior to state of the art in vessel segmentation task. We tested this work on two public datasets for blood vessel segmentation in retinal images showing that this work outperforms many of other contributions by other authors.
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