EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
EAD-Net: A Novel Lesion Segmentation Method in Diabetic Retinopathy Using Neural Networks
Authors
Keywords
-
Journal
DISEASE MARKERS
Volume 2021, Issue -, Pages 1-13
Publisher
Hindawi Limited
Online
2021-09-03
DOI
10.1155/2021/6482665
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine
- (2019) Syna Sreng et al. IEEJ Transactions on Electrical and Electronic Engineering
- Retinal image assessment using bi-level adaptive morphological component analysis
- (2019) Malihe Javidi et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
- L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images
- (2019) Song Guo et al. NEUROCOMPUTING
- A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images
- (2019) Clement Playout et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Clinical Report Guided Retinal Microaneurysm Detection With Multi-Sieving Deep Learning
- (2018) Ling Dai et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Detection of exudates in fundus photographs with imbalanced learning using conditional generative adversarial network
- (2018) Rui Zheng et al. Biomedical Optics Express
- Deep image mining for diabetic retinopathy screening
- (2017) Gwenolé Quellec et al. MEDICAL IMAGE ANALYSIS
- Detection of hard exudates using mean shift and normalized cut method
- (2016) Sreeparna Banerjee et al. Biocybernetics and Biomedical Engineering
- A novel method for retinal exudate segmentation using signal separation algorithm
- (2016) Elaheh Imani et al. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
- Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening
- (2016) Lama Seoud et al. IEEE TRANSACTIONS ON MEDICAL IMAGING
- Exudate segmentation in fundus images using an ant colony optimization approach
- (2015) Carla Pereira et al. INFORMATION SCIENCES
- Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System
- (2015) T. Jaya et al. JOURNAL OF DIGITAL IMAGING
- Automatic exudate detection by fusing multiple active contours and regionwise classification
- (2014) Balazs Harangi et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Exudate detection in color retinal images for mass screening of diabetic retinopathy
- (2014) Xiwei Zhang et al. MEDICAL IMAGE ANALYSIS
- An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading
- (2012) B. Antal et al. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started