ArticleBMC ophthalmology2020
Classification of optical coherence tomography images using a capsule network.
Article in BMC ophthalmology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review.Eye (London, England) · 2023Pooled it
- A two-stage deep learning framework with progressive feature fusion and attention mechanisms for multi-class retinal disease classification.Frontiers in artificial intelligence · 2026Article
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- Self-AttentionNeXt: Exploring schizophrenic optical coherence tomography image detection investigations.World journal of psychiatry · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- MSLI-Net: retinal disease detection network based on multi-segment localization and multi-scale interaction.Frontiers in cell and developmental biology · 2025Article
- HyReti-Net: hybrid retinal diseases classification and diagnosis network using optical coherence tomography.Frontiers in medicine · 2025Article
- Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model.PloS one · 2025Article
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Classification of diabetic maculopathy based on optical coherence tomography images using a Vision Transformer model.BMJ open ophthalmology · 2023Article
- Few-shot out-of-distribution detection for automated screening in retinal OCT images using deep learning.Scientific reports · 2023Article
- Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture.Bioengineering (Basel, Switzerland) · 2023Article
- Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- An interpretable transformer network for the retinal disease classification using optical coherence tomography.Scientific reports · 2023Article
- A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers.Frontiers in cell and developmental biology · 2023Article
- Automated artificial intelligence-based system for clinical follow-up of patients with age-related macular degeneration.Acta ophthalmologica · 2022Article
- Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images.Scientific data · 2022Article
- Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.Translational vision science & technology · 2022Article
- Automatic detection of microaneurysms in optical coherence tomography images of retina using convolutional neural networks and transfer learning.Scientific reports · 2022Article
- HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification.Biosensors · 2022Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundClassification of optical coherence tomography (OCT) images can be achieved with high accuracy using classical convolution neural networks (CNN), a commonly used deep learning network for computer-aided diagnosis. Classical CNN has often been criticized for suppressing positional relations in a pooling layer. Therefore, because capsule networks can learn positional information from images, we attempted application of a capsule network to OCT images to overcome that shortcoming. This study is our attempt to improve classification accuracy by replacing CNN with a capsule network.
methodsFrom an OCT dataset, we produced a training dataset of 83,484 images and a test dataset of 1000 images. For training, the dataset comprises 37,205 images with choroidal neovascularization (CNV), 11,348 with diabetic macular edema (DME), 8616 with drusen, and 26,315 normal images. The test dataset has 250 images from each category. The proposed model was constructed based on a capsule network for improving classification accuracy. It was trained using the training dataset. Subsequently, the test dataset was used to evaluate the trained model.
resultsClassification of OCT images using our method achieved accuracy of 99.6%, which is 3.2 percentage points higher than that of other methods described in the literature.
conclusionThe proposed method achieved classification accuracy results equivalent to those reported for other methods for CNV, DME, drusen, and normal images.
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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.