ArticleDiagnostics (Basel, Switzerland)2023
Hybrid Methods for Fundus Image Analysis for Diagnosis of Diabetic Retinopathy Development Stages Based on Fusion 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 12 papers.
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12 citing papers in PubMed, 13 citations in OpenAlex.
- An ensemble of deep learning models with falcon optimization assisted diabetic retinopathy diagnosis on retinal fundus images.Scientific reports · 2026Article
- ScaHybNet: a scalogram-based hybrid ensemble network for ECG arrhythmia classification.Scientific reports · 2026Article
- Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images.Quantitative imaging in medicine and surgery · 2026Article
- Deep residual network fusing CT images and clinical variables to predict lung adenocarcinoma aggressiveness.BMC medical imaging · 2026Article
- Syncretic Grad-CAM Integrated ViT-CNN Hybrids with Inherent Explainability for Early Thyroid Cancer Diagnosis from Ultrasound.Diagnostics (Basel, Switzerland) · 2026Article
- An enhanced diabetic retinopathy detection approach using optimized deep learning technique.Scientific reports · 2026Article
- Interpretable lung-constrained RegNetY-ViT framework for pulmonary tuberculosis classification in chest X-rays with radiological feature-guided neuro-symbolic reasoning.Frontiers in medicine · 2026Article
- DRCNN-Lesion Proxy: a hybrid CNN with lesion-inspired feature simulation for diabetic retinopathy severity classification.Scientific reports · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Beyond Post hoc Explanations: A Comprehensive Framework for Accountable AI in Medical Imaging Through Transparency, Interpretability, and Explainability.Bioengineering (Basel, Switzerland) · 2025Review
- Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.Scientific reports · 2025Article
- Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches.Scientific reports · 2025Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Diabetic retinopathy (DR) is a complication of diabetes that damages the delicate blood vessels of the retina and leads to blindness. Ophthalmologists rely on diagnosing the retina by imaging the fundus. The process takes a long time and needs skilled doctors to diagnose and determine the stage of DR. Therefore, automatic techniques using artificial intelligence play an important role in analyzing fundus images for the detection of the stages of DR development. However, diagnosis using artificial intelligence techniques is a difficult task and passes through many stages, and the extraction of representative features is important in reaching satisfactory results. Convolutional Neural Network (CNN) models play an important and distinct role in extracting features with high accuracy. In this study, fundus images were used for the detection of the developmental stages of DR by two proposed methods, each with two systems. The first proposed method uses GoogLeNet with SVM and ResNet-18 with SVM. The second method uses Feed-Forward Neural Networks (FFNN) based on the hybrid features extracted by first using GoogLeNet, Fuzzy color histogram (FCH), Gray Level Co-occurrence Matrix (GLCM), and Local Binary Pattern (LBP); followed by ResNet-18, FCH, GLCM and LBP. All the proposed methods obtained superior results. The FFNN network with hybrid features of ResNet-18, FCH, GLCM, and LBP obtained 99.7% accuracy, 99.6% precision, 99.6% sensitivity, 100% specificity, and 99.86% AUC.
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