loading
Papers Papers/2020

Research.Publish.Connect.

Paper

Authors: Amir Panahi 1 ; Reza Moghadam 1 and Kurosh Madani 2

Affiliations: 1 Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran ; 2 LISSI Lab, Senart-FB Institute of Technology, University Paris Est-Creteil (UPEC), Lieusaint, France

ISBN: 978-989-758-441-1

Keyword(s): Deep Learning, Residual-like CNN, Computer Vision, Image Segmentation, Glaucoma Detection, Eye Fundus, Optic Disc Segmentation, Medical Application.

Abstract: Eye diseases such as glaucoma, if undiagnosed in time, can have irreversible detrimental effects, which can lead to blindness. Early detection of this disease by screening programs and subsequent treatment can prevent blindness. Deep learning architectures have many applications in medicine, especially in medical image processing, that provides intelligent tools for the prevention and treatment of diseases. Optic disk segmentation is one of the ways to diagnose eye disease. This paper presents a new approach based on deep learning, which is accurate and fast in optic disc segmentation. By Comparison proposed method with the best-known methods on publicly available databases DRIONS-DB, RIM-ONE v.3, the proposed algorithm is much faster, which can segment the optic disc in 0.008 second with outstanding performance concerning IOU and DICE scores. Therefore, this method can be used in ophthalmology clinics to segment the optic disc in retina images and videos as online medical assistive t ool. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.249.17

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Panahi, A.; Moghadam, R. and Madani, K. (2020). Deep Learning Residual-like Convolutional Neural Networks for Optic Disc Segmentation in Medical Retinal Images.In Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA, ISBN 978-989-758-441-1, pages 23-29. DOI: 10.5220/0009799100230029

@conference{delta20,
author={Amir Panahi. and Reza Moghadam. and Kurosh Madani.},
title={Deep Learning Residual-like Convolutional Neural Networks for Optic Disc Segmentation in Medical Retinal Images},
booktitle={Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,},
year={2020},
pages={23-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009799100230029},
isbn={978-989-758-441-1},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Deep Learning Theory and Applications - Volume 1: DeLTA,
TI - Deep Learning Residual-like Convolutional Neural Networks for Optic Disc Segmentation in Medical Retinal Images
SN - 978-989-758-441-1
AU - Panahi, A.
AU - Moghadam, R.
AU - Madani, K.
PY - 2020
SP - 23
EP - 29
DO - 10.5220/0009799100230029

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.