ArticleSensors (Basel, Switzerland)2023
Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.
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 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy and clinical performance of deep learning models for grading diabetic retinopathy: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- A hybrid CNN-GNN and multitask learning pipeline for improving mild diabetic retinopathy sensitivity.MethodsX · 2026Article
- Immune Repertoire Profiling Reveals Distinct Adaptive Immune Signatures of Dampness ZHENG Across Psoriasis, Rheumatoid Arthritis and Ulcerative Colitis.Cell proliferation · 2026Article
- Toward Reliable Diabetic Retinopathy Screening.Sensors (Basel, Switzerland) · 2026Article
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- A Novel Convolutional Neural Network for Explainable Diabetic Retinopathy Detection and Grade Identification.Sensors (Basel, Switzerland) · 2026Article
- An explainable ensemble for diabetic retinopathy grading with a novel confidence quality factor and configurable heatmaps.Medical & biological engineering & computing · 2026Article
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
- An Improved Microaneurysms Detection for Diabetic Retinopathy Screening Using YOLO.Biomedicines · 2026Article
- Artificial intelligence for early detection of diabetic retinopathy: A vision transformer-based approach.PloS one · 2026Article
- Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images.Scientific reports · 2025Article
- Graph-enhanced deep learning for diabetic retinopathy diagnosis: A quality-aware and uncertainty-driven approach.PLoS computational biology · 2025Article
- Assessing ResNeXt and RegNet Models for Diabetic Retinopathy Classification: A Comprehensive Comparative Study.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing pathological feature discrimination in diabetic retinopathy multi-classification with self-paced progressive multi-scale training.Scientific reports · 2025Article
- A Comparative Study of Lesion-Centered and Severity-Based Approaches to Diabetic Retinopathy Classification: Improving Interpretability and Performance.Biomedicines · 2025Article
- Vision transformer based interpretable metabolic syndrome classification using retinal Images.NPJ digital medicine · 2025Article
- A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.Scientific reports · 2025Article
- Visual impairment prevention by early detection of diabetic retinopathy based on stacked auto-encoder.Scientific reports · 2025Article
- Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches.Scientific reports · 2025Article
- Deep learning based binary classification of diabetic retinopathy images using transfer learning approach.Journal of diabetes and metabolic disorders · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.
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