ArticleSensors (Basel, Switzerland)2023
Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Microaneurysm segmentation in early diabetic retinopathy: a localized patch-based U-Net approach to overcome microscopic lesion oversight.International journal of retina and vitreous · 2026Article
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Article
- Grey wolf optimization technique with U-shaped and capsule networks-A novel framework for glaucoma diagnosis.MethodsX · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
In this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++. The performance evaluation is based on the calculation of the Dice score and IoU values. The ensemble-based model achieved higher Dice score (0.95) and IoU (0.91) values compared to other network architectures. The proposed ensemble-based model demonstrates the high practical application potential for detection of early-stage diabetic retinopathy in color fundus images.
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Registered trials
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