ArticleComputational intelligence and neuroscience2023
Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.
Article in Computational intelligence and neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 100 citations in OpenAlex.
- A Text-Guided Lesion Mining Vision-Language Ordinal Classification Framework for Diabetic Retinopathy Grading.Journal of imaging · 2026Article
- Explicit Inclusion of Diabetes Mellitus Without Retinopathy Within Diabetic Retinopathy Prediction.Translational vision science & technology · 2026Article
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
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- REFA-DINO: A Relative-Efficient Fusion Attention Framework for Binary and Multiclass Diabetic Retinopathy Classification.International journal of biomedical imaging · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Integrating non-linear radon transformation for diabetic retinopathy grading.Scientific reports · 2025Article
- Generative artificial intelligence in diabetes healthcare.iScience · 2025Review
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
- Research on grading detection methods for diabetic retinopathy based on deep learning.Pakistan journal of medical sciences · 2025Article
- External Validation of Deep Learning Models for Classifying Etiology of Retinal Hemorrhage Using Diverse Fundus Photography Datasets.Bioengineering (Basel, Switzerland) · 2024Article
- Research trends and hotspots in fundus image segmentation from 2007 to 2023: A bibliometric analysis.Heliyon · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Fine-grained attention & knowledge-based collaborative network for diabetic retinopathy grading.Heliyon · 2023Article
- Article
- Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review.Journal of imaging · 2023Review
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
- Vision transformers: The next frontier for deep learning-based ophthalmic image analysis.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a common retinal vascular disease, which can cause severe visual impairment. It is of great clinical significance to use fundus images for intelligent diagnosis of DR. In this paper, an intelligent DR classification model of fundus images is proposed. This method can detect all the five stages of DR, including of no DR, mild, moderate, severe, and proliferative. This model is composed of two key modules. FEB, feature extraction block, is mainly used for feature extraction of fundus images, and GPB, grading prediction block, is used to classify the five stages of DR. The transformer in the FEB has more fine-grained attention that can pay more attention to retinal hemorrhage and exudate areas. The residual attention in the GPB can effectively capture different spatial regions occupied by different classes of objects. Comprehensive experiments on DDR datasets well demonstrate the superiority of our method, and compared with the benchmark method, our method has achieved competitive performance.
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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.