ArticleIEEE transactions on bio-medical engineering2021
DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Interpretable Deep Learning for OCT-Based Diagnosis of Vitreoretinal Lymphoma Versus Uveitis.Translational vision science & technology · 2026Article
- A Hierarchical Deep Learning Architecture for Diagnosing Retinal Diseases Using Cross-Modal OCT to Fundus Translation in the Lack of Paired Data.Journal of imaging · 2026Article
- D-TNet: a hybrid Dense Net-transformer model for robust diabetic retinopathy detection.Scientific reports · 2025Article
- DRCNN-Lesion Proxy: a hybrid CNN with lesion-inspired feature simulation for diabetic retinopathy severity classification.Scientific reports · 2025Article
- Equitable Deep Learning for Diabetic Retinopathy Detection Using Multidimensional Retinal Imaging With Fair Adaptive Scaling.Translational vision science & technology · 2025Article
- Advancing Diabetic Retinopathy Screening: A Systematic Review of Artificial Intelligence and Optical Coherence Tomography Angiography Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Article
- AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography.Biomedical optics express · 2024Article
- Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.Eye (London, England) · 2024Observational
- CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.Quantitative imaging in medicine and surgery · 2024Article
- Ocular microvascular complications in diabetic retinopathy: insights from machine learning.BMJ open diabetes research & care · 2024Article
- Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map.IEEE transactions on bio-medical engineering · 2024Article
- Vision-Related Quality of Life Among Diabetic Retinopathy Patients in a Hospital-Based Population in the Sultanate of Oman.Clinical optometry · 2024Article
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
- A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach.Scientific reports · 2023Article
- Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy.Biomedical optics express · 2023Article
- OCT angiography and its retinal biomarkers [Invited].Biomedical optics express · 2023Review
- Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion Features.Diagnostics (Basel, Switzerland) · 2023Article
- Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT.Ophthalmology science · 2023Article
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7 authors.
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Abstract
objectiveOptical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automated, complete DR classification frameworks based on both OCT and OCTA data have not been proposed. In this study, a convolutional neural network (CNN) based method is proposed to fulfill a DR classification framework using en face OCT and OCTA.
methodsA densely and continuously connected neural network with adaptive rate dropout (DcardNet) is designed for the DR classification. In addition, adaptive label smoothing was proposed and used to suppress overfitting. Three separate classification levels are generated for each case based on the International Clinical Diabetic Retinopathy scale. At the highest level the network classifies scans as referable or non-referable for DR. The second level classifies the eye as non-DR, non-proliferative DR (NPDR), or proliferative DR (PDR). The last level classifies the case as no DR, mild and moderate NPDR, severe NPDR, and PDR.
resultsWe used 10-fold cross-validation with 10% of the data to assess the network's performance. The overall classification accuracies of the three levels were 95.7%, 85.0%, and 71.0% respectively. CONCLUSION/SIGNIFICANCE: A reliable, sensitive and specific automated classification framework for referral to an ophthalmologist can be a key technology for reducing vision loss related to DR.
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