ArticleDiagnostics (Basel, Switzerland)2023
A Hybrid Technique for Diabetic Retinopathy Detection Based on Ensemble-Optimized CNN and Texture Features.
Article in Diagnostics (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, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Radiomics in ophthalmology: a systematic review.European radiology · 2025Pooled it
- Dual branch fundus deep learning network as an enhanced multi classification system for ocular disease detection via hybrid feature fusion.Scientific reports · 2026Article
- A Robust Intelligent CNN Model Enhanced with Gabor-Based Feature Extraction, SMOTE Balancing, and Adam Optimization for Multi-Grade Diabetic Retinopathy Classification.Journal of imaging · 2026Article
- An enhanced diabetic retinopathy detection approach using optimized deep learning technique.Scientific reports · 2026Article
- Diabetic retinopathy severity detection using an improved Whale optimization algorithm and convolutional Kolmogorov-Arnold network.Frontiers in medicine · 2026Article
- Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images.Scientific reports · 2025Article
- Knowledge distillation-based lightweight MobileNet model for diabetic retinopathy classification.Scientific reports · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.Scientific reports · 2025Article
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
- Bibliometric analysis of ophthalmic OCT and OCT angiography research trends over the past 20 years.International ophthalmology · 2024Article
- Application of deep learning and feature selection technique on external root resorption identification on CBCT images.BMC oral health · 2024Article
- FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy.PeerJ. Computer science · 2024Article
- A Lightweight Diabetic Retinopathy Detection Model Using a Deep-Learning Technique.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most prevalent chronic conditions that can result in permanent vision loss is diabetic retinopathy (DR). Diabetic retinopathy occurs in five stages: no DR, and mild, moderate, severe, and proliferative DR. The early detection of DR is essential for preventing vision loss in diabetic patients. In this paper, we propose a method for the detection and classification of DR stages to determine whether patients are in any of the non-proliferative stages or in the proliferative stage. The hybrid approach based on image preprocessing and ensemble features is the foundation of the proposed classification method. We created a convolutional neural network (CNN) model from scratch for this study. Combining Local Binary Patterns (LBP) and deep learning features resulted in the creation of the ensemble features vector, which was then optimized using the Binary Dragonfly Algorithm (BDA) and the Sine Cosine Algorithm (SCA). Moreover, this optimized feature vector was fed to the machine learning classifiers. The SVM classifier achieved the highest classification accuracy of 98.85% on a publicly available dataset, i.e., Kaggle EyePACS. Rigorous testing and comparisons with state-of-the-art approaches in the literature indicate the effectiveness of the proposed methodology.
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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.