ArticleSensors (Basel, Switzerland)2022
Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Two parallel systems constructed from complementary optical coherence tomography and histopathology for the assessment of murine intestinal inflammation: a dual-modality assessment.Quantitative imaging in medicine and surgery · 2026Article
- Early Diabetic Retinopathy Detection from OCT Images Using Multifractal Analysis and Multi-Layer Perceptron Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Diabetic Retinopathy-A Review.Current diabetes reviews · 2025Review
- Article
- An Extremely Sparse Tomography Reconstruction of a Multispectral Temperature Field without Any a Priori Knowledge.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Rigid alignment method for secondary analyses of optical coherence tomography volumes.Biomedical optics express · 2024Article
- A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images.Scientific reports · 2024Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- The application of artificial intelligence in diabetic retinopathy: progress and prospects.Frontiers in cell and developmental biology · 2024Review
- Wavelet scattering transform application in classification of retinal abnormalities using OCT images.Scientific reports · 2023Article
- Review
- Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders.Bioengineering (Basel, Switzerland) · 2023Article
- Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture.Bioengineering (Basel, Switzerland) · 2023Article
- Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding.Diagnostics (Basel, Switzerland) · 2023Article
- Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2023Article
- On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.Bioengineering (Basel, Switzerland) · 2023Review
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
- Evaluation of Structural Retinal Layer Alterations in Retinitis Pigmentosa.Romanian journal of ophthalmologyArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.
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Registered trials
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.