ArticleLife (Basel, Switzerland)2022
Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed, 25 citations in OpenAlex.
- Knowledge Distillation and Explainability Analysis for Lightweight Retinal Disease Classification Using MultiEYE Fundus Images.Diagnostics (Basel, Switzerland) · 2026Article
- Non-invasive screening of alzheimer's disease via label-free tri-spectral retinal imaging.Scientific reports · 2026Article
- Visualization of the Persistent Avascular Retina with Ultra-Widefield Green Reflectance Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- A novel methodology based on Contrast-Adaptive Threshold Model for accurate optic disc detection in retinal images of premature infants.Physical and engineering sciences in medicine · 2025Article
- A comparation of three different anti-VEGF drugs in development of persistent avascular retina in premature children.Scientific reports · 2024Article
- Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.Scientific data · 2024Article
- Unfolded deep kernel estimation-attention UNet-based retinal image segmentation.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
Color fundus photographs are the most common type of image used for automatic diagnosis of retinal diseases and abnormalities. As all color photographs, these images contain information about three primary colors, i.e., red, green, and blue, in three separate color channels. This work aims to understand the impact of each channel in the automatic diagnosis of retinal diseases and abnormalities. To this end, the existing works are surveyed extensively to explore which color channel is used most commonly for automatically detecting four leading causes of blindness and one retinal abnormality along with segmenting three retinal landmarks. From this survey, it is clear that all channels together are typically used for neural network-based systems, whereas for non-neural network-based systems, the green channel is most commonly used. However, from the previous works, no conclusion can be drawn regarding the importance of the different channels. Therefore, systematic experiments are conducted to analyse this. A well-known U-shaped deep neural network (U-Net) is used to investigate which color channel is best for segmenting one retinal abnormality and three retinal landmarks.
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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.