ArticleBrain sciences2022
Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed, 92 citations in OpenAlex.
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
- Article
- Enhancing AI-based diabetic retinopathy diagnosis through universal cross-camera image adaptation.BMJ open ophthalmology · 2025Article
- Adaptive Multimodal Fusion in Vertical Federated Learning for Decentralized Glaucoma Screening.Brain sciences · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Mamba-fusion for privacy-preserving disease prediction.Scientific reports · 2025Article
- A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification.Scientific reports · 2025Article
- A deep learning based model for diabetic retinopathy grading.Scientific reports · 2025Article
- Microvascular Metrics on Diabetic Retinopathy Severity: Analysis of Diabetic Eye Images from Real-World Data.Biomedicines · 2024Article
- Deep learning based binary classification of diabetic retinopathy images using transfer learning approach.Journal of diabetes and metabolic disorders · 2024Article
- A retinal detachment based strabismus detection through FEDCNN.Scientific reports · 2024Article
- Correction: Jabbar et al. Transfer Learning-Based Model for Diabetic Retinopathy Diagnosis Using Retinal Images.Brain sciences · 2024Article
- Concatenated Modified LeNet Approach for Classifying Pneumonia Images.Journal of personalized medicine · 2024Article
- A Hybrid Technique for Diabetic Retinopathy Detection Based on Ensemble-Optimized CNN and Texture Features.Diagnostics (Basel, Switzerland) · 2023Article
- Development of revised ResNet-50 for diabetic retinopathy detection.BMC bioinformatics · 2023Article
- A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2023Review
- Global trends and performances in diabetic retinopathy studies: A bibliometric analysis.Frontiers in public health · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Diabetic retinopathy (DR) is a visual obstacle caused by diabetic disease, which forms because of long-standing diabetes mellitus, which damages the retinal blood vessels. This disease is considered one of the principal causes of sightlessness and accounts for more than 158 million cases all over the world. Since early detection and classification could diminish the visual impairment, it is significant to develop an automated DR diagnosis method. Although deep learning models provide automatic feature extraction and classification, training such models from scratch requires a larger annotated dataset. The availability of annotated training datasets is considered a core issue for implementing deep learning in the classification of medical images. The models based on transfer learning are widely adopted by the researchers to overcome annotated data insufficiency problems and computational overhead. In the proposed study, features are extracted from fundus images using the pre-trained network VGGNet and combined with the concept of transfer learning to improve classification performance. To deal with data insufficiency and unbalancing problems, we employed various data augmentation operations differently on each grade of DR. The results of the experiment indicate that the proposed framework (which is evaluated on the benchmark dataset) outperformed advanced methods in terms of accurateness. Our technique, in combination with handcrafted features, could be used to improve classification 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.