Automated diagnosis of Retinopathy of prematurity from retinal images of preterm infants using hybrid deep learning techniques
Author:
Publisher
Elsevier BV
Subject
Health Informatics,Signal Processing,Biomedical Engineering
Reference78 articles.
1. Retinopathy of prematurity: a review of risk factors and their clinical significance;Kim;Surv Ophthalmol,2018
2. Proliferative Vitreoretinopathy: A Review;Idrees;Int Ophthalmol Clin,2019
3. Preterm-associated visual impairment and estimates of retinopathy of prematurity at regional and global levels for 2010;Blencowe;Pediatr Res,2013
4. Retinopathy of prematurity: a global perspective of the epidemics, population of babies at risk and implications for control;Gilbert;Early Hum Dev,2008
5. Update on Blindness Due to Retinopathy of Prematurity Globally and in India;Blencowe;Indian Pediatr,2016
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1. Comprehensive Prediction of Retinopathy in Preterm Infants Using Deep Learning Approaches;Advances in Computer and Electrical Engineering;2024-08-30
2. A Hybrid Framework for Retinal Image Enhancement on Local DR Data Using ECLAHE and IWF;Lecture Notes in Networks and Systems;2024
3. A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks;Healthcare Analytics;2023-12
4. Smartphone Fundus Photography Enhancement for Retinopathy of Prematurity Disease Diagnosis Using Deep Learning;2023 6th International Conference of Computer and Informatics Engineering (IC2IE);2023-09-14
5. Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning;Applied Sciences;2023-04-07
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