ArticlePeerJ. Computer science2024
Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- A gated task-attentive multi-task network for unified retinal image analysis.Scientific reports · 2026Article
- A novel method for optic disc localization using fast circlet transform and Chan-Vese segmentation.Scientific reports · 2025Article
- CurriMAE: curriculum learning based masked autoencoders for multi-labeled pediatric thoracic disease classification.PeerJ. Computer science · 2025Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model's ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.
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