ArticleSensors (Basel, Switzerland)2023
Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.
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 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Functional Shape Framework for the Detection of Multiple Sclerosis Using Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2026Article
- Ensemble learning-based method for multiple sclerosis screening from retinal OCT images.Medical & biological engineering & computing · 2025Article
- A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.Journal of healthcare informatics research · 2025Article
- HyReti-Net: hybrid retinal diseases classification and diagnosis network using optical coherence tomography.Frontiers in medicine · 2025Article
- Article
- Detection of Disease Features on Retinal OCT Scans Using RETFound.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of Deep Learning Techniques in Healthcare Systems: A Review.Journal of clinical practice and research · 2024Review
- Identifying retinopathy in optical coherence tomography images with less labeled data via contrastive graph regularization.Biomedical optics express · 2024Article
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Multiscale attention-over-attention network for retinal disease recognition in OCT radiology images.Frontiers in medicine · 2024Article
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Multi-Scale Learning with Sparse Residual Network for Explainable Multi-Disease Diagnosis in OCT Images.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome.Diagnostics (Basel, Switzerland) · 2023Article
- Special Issue: "Intelligent Systems for Clinical Care and Remote Patient Monitoring".Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
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Abstract
Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study's training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew's correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.
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