ArticleSensors (Basel, Switzerland)2023
Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A review of optimization strategies for deep and machine learning in diabetic macular edema.Frontiers in artificial intelligence · 2026Pooled it
- A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.BMC ophthalmology · 2025Pooled it
- Self-Supervised OCT Representation Learning with Local Dimensionality Regularization for Automated Retinal Disease Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion.Scientific data · 2026Article
- Optical Coherence Tomography, Stereomicroscopic, and Histological Aspects of Bone Regeneration on Rat Calvaria in the Presence of Bovine Xenograft or Titanium-Reinforced Hydroxyapatite.Journal of functional biomaterials · 2026Article
- A two-stage deep learning framework with progressive feature fusion and attention mechanisms for multi-class retinal disease classification.Frontiers in artificial intelligence · 2026Article
- DRCNN-Lesion Proxy: a hybrid CNN with lesion-inspired feature simulation for diabetic retinopathy severity classification.Scientific reports · 2025Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- Human fall direction recognition in the indoor and outdoor environment using multi self-attention RBnet deep architectures and tree seed optimization.Scientific reports · 2025Article
- Classifying retinal diseases via pyramid vision graph convolutional network for optical coherence tomography images.Biomedical optics express · 2025Article
- HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification.Scientific reports · 2025Article
- Deep Learning to Distinguish Edema Secondary to Retinal Vein Occlusion and Diabetic Macular Edema: A Multimodal Approach Using OCT and Infrared Imaging.Journal of clinical medicine · 2025Article
- Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease ofSensors (Basel, Switzerland) · 2024Article
- Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.PloS one · 2024Article
- Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images.Diagnostics (Basel, Switzerland) · 2023Article
- Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks.Journal of imaging · 2023Article
- Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.
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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.