ArticleDiagnostics (Basel, Switzerland)2022
Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Transfer Learning and Optimized Machine Learning Techniques for Multiclass Diabetic Retinopathy Classification Using Retinal Images.Diagnostics (Basel, Switzerland) · 2026Article
- Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification.Journal of biomedical physics & engineering · 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
- HyperGraph-based capsule temporal memory network for efficient and explainable diabetic retinopathy detection in retinal imaging.Scientific reports · 2025Article
- D-TNet: a hybrid Dense Net-transformer model for robust diabetic retinopathy detection.Scientific reports · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization.MethodsX · 2025Article
- Diabetic retinopathy detection based on mobile maxout network and weber local descriptor feature selection using retinal fundus image.Scientific reports · 2025Article
- Vision transformer based interpretable metabolic syndrome classification using retinal Images.NPJ digital medicine · 2025Article
- Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and ChiScientific reports · 2025Article
- A deep learning based model for diabetic retinopathy grading.Scientific reports · 2025Article
- Optimising deep learning models for ophthalmological disorder 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
- A comparative study of machine learning models for automated detection and classification of retinal diseases in Ghana.PloS one · 2025Article
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features.Diagnostics (Basel, Switzerland) · 2023Article
- Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences.Diagnostics (Basel, Switzerland) · 2023Article
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5 authors.
Funding
Abstract
Diabetic Retinopathy (DR) is a medical condition present in patients suffering from long-term diabetes. If a diagnosis is not carried out at an early stage, it can lead to vision impairment. High blood sugar in diabetic patients is the main source of DR. This affects the blood vessels within the retina. Manual detection of DR is a difficult task since it can affect the retina, causing structural changes such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HMs), and extra blood vessel growth. In this work, a hybrid technique for the detection and classification of Diabetic Retinopathy in fundus images of the eye is proposed. Transfer learning (TL) is used on pre-trained Convolutional Neural Network (CNN) models to extract features that are combined to generate a hybrid feature vector. This feature vector is passed on to various classifiers for binary and multiclass classification of fundus images. System performance is measured using various metrics and results are compared with recent approaches for DR detection. The proposed method provides significant performance improvement in DR detection for fundus images. For binary classification, the proposed modified method achieved the highest accuracy of 97.8% and 89.29% for multiclass classification.
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