ArticleGenes2019
Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 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
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy.Scientific reports · 2023Article
- A hybrid neural network approach for classifying diabetic retinopathy subtypes.Frontiers in medicine · 2023Article
- MSLF-Net: A Multi-Scale and Multi-Level Feature Fusion Net for Diabetic Retinopathy Segmentation.Diagnostics (Basel, Switzerland) · 2022Article
- Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the attention mechanism, multiple feature layers with obvious target features are fused to achieve preliminary MA detection. Finally, the spatial relationships between MAs and blood vessels are utilized to perform a secondary screening of the preliminary test results to obtain the final MA detection results. We evaluated the method on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The results show that our method effectively improves the average accuracy and sensitivity of MA detection.
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Registered trials
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