Retinal Vessel Segmentation by Inception-like Convolutional Neural Networks

Hadi Shirvan, Reza Moghadam, Kurosh Madani

2020

Abstract

Deep learning architectures have been proposed in some neural networks like convolutional neural networks (CNN), recurrent neural networks and deep belief neural networks. Among them, CNNs have been applied in image processing tasks frequently. An important section in intelligent image processing is medical image processing which provides intelligent tools and software for medical applications. Analysis of blood vessels in retinal images would help the physicians to detect some retina diseases like glaucoma or even diabetes. In this paper a new neural network structure is proposed which can process the retinal images and detect vessels apart from retinal background. This neural network consists of convolutional layers, concatenate layers and transpose convolutional layers. The results for DRIVE dataset show acceptable performance regarding to accuracy, recall and F-measure criteria.

Paper Citation

in Harvard Style

Shirvan H., Moghadam R. and Madani K. (2020). Retinal Vessel Segmentation by Inception-like Convolutional Neural Networks.In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-441-1, pages 53-58. DOI: 10.5220/0009638100530058

in Bibtex Style

@conference{delta20,
author={Hadi Shirvan and Reza Moghadam and Kurosh Madani},
title={Retinal Vessel Segmentation by Inception-like Convolutional Neural Networks},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2020},
pages={53-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009638100530058},
isbn={978-989-758-441-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Retinal Vessel Segmentation by Inception-like Convolutional Neural Networks
SN - 978-989-758-441-1
AU - Shirvan H.
AU - Moghadam R.
AU - Madani K.
PY - 2020
SP - 53
EP - 58
DO - 10.5220/0009638100530058