ArticleJournal of ophthalmology2020
Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network.
Article in Journal of ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 108 citations in OpenAlex.
- An ensemble of deep learning models with falcon optimization assisted diabetic retinopathy diagnosis on retinal fundus images.Scientific reports · 2026Article
- Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Wavelet scattering transform application in classification of retinal abnormalities using OCT images.Scientific reports · 2023Article
- Influence of feedforward control-based health education intervention on compliance, visual function and self-perceived burden among patients with diabetic retinopathy.African health sciences · 2023Article
- CLRD: Collaborative Learning for Retinopathy Detection Using Fundus Images.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Humming Bird Optimization-Based Hybrid CNN-RNN for Accurate Exudate Classification from Fundus Images.Journal of digital imaging · 2023Article
- Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.PloS one · 2023Article
- Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.Journal of healthcare engineering · 2023Review
- Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy.Health information science and systems · 2022Article
- Optical coherence tomography image based eye disease detection using deep convolutional neural network.Health information science and systems · 2022Article
- Machine Learning for Electrocardiographic Features to Identify Left Atrial Enlargement in Young Adults: CHIEF Heart Study.Frontiers in cardiovascular medicine · 2022Article
- Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.Journal of imaging · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning of fundus photograph has emerged as a practical and cost-effective technique for automatic screening and diagnosis of severer diabetic retinopathy (DR). The entropy image of luminance of fundus photograph has been demonstrated to increase the detection performance for referable DR using a convolutional neural network- (CNN-) based system. In this paper, the entropy image computed by using the green component of fundus photograph is proposed. In addition, image enhancement by unsharp masking (UM) is utilized for preprocessing before calculating the entropy images. The bichannel CNN incorporating the features of both the entropy images of the gray level and the green component preprocessed by UM is also proposed to improve the detection performance of referable DR by deep learning.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.