A novel retinal vessel detection approach based on multiple deep convolution neural networks.

Journal: Computer methods and programs in biomedicine

Published Date: Oct 30, 2018

Abstract

BACKGROUND AND OBJECTIVE: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detection is still a challenging problem due to variations in morphology of the vessels on noisy and low contrast fundus images.

Authors

Yanhui Guo

Department of Computer Science, University of Illinois Springfield, Springfield, IL, United States.
Ümit Budak

Department of Biology, Art and Science Faculty, Bozok University, 66100 Yozgat, Turkey.
Abdulkadir Şengür

Electrical and Electronics Engineering Department, Firat University, Elazig, Turkey.

Keywords

Algorithms Computer Systems Diagnosis, Computer-Assisted Fundus Oculi Humans Machine Learning Models, Statistical Neural Networks, Computer Reproducibility of Results Retinal Diseases Retinal Vessels Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (30501859)

A novel retinal vessel detection approach based on multiple deep convolution neural networks.

Abstract

Authors

Keywords

External Resources

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