ArticleMultimedia tools and applications2022
Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC).
Article in Multimedia tools and applications, 2022. 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, 131 citations in OpenAlex.
- Use of Artificial Intelligence in the Interpretation of Electroretinography (ERG) Studies.International journal of molecular sciences · 2026Pooled it
- 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
- Enhanced diabetes prediction using pre-trained CNNs, LSTM, and conditional GAN on transformed numerical data.Scientific reports · 2026Article
- Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal integration.Frontiers in medicine · 2026Review
- Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images.Scientific reports · 2025Article
- A Lightweight CNN for Multiclass Retinal Disease Screening with Explainable AI.Journal of imaging · 2025Article
- Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.Scientific reports · 2025Article
- Artificial intelligence for sustainable farming with dual branch convolutional graph attention networks in rice leaf disease detection.Scientific reports · 2025Article
- A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis.Journal of healthcare informatics research · 2025Article
- Deep learning based binary classification of diabetic retinopathy images using transfer learning approach.Journal of diabetes and metabolic disorders · 2024Article
- Review
- FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy.PeerJ. Computer science · 2024Article
- Design of a 3D emotion mapping model for visual feature analysis using improved Gaussian mixture models.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic Retinopathy (DR) is caused as a result of Diabetes Mellitus which causes development of various retinal abrasions in the human retina. These lesions cause hindrance in vision and in severe cases, DR can lead to blindness. DR is observed amongst 80% of patients who have been diagnosed from prolonged diabetes for a period of 10-15 years. The manual process of periodic DR diagnosis and detection for necessary treatment, is time consuming and unreliable due to unavailability of resources and expert opinion. Therefore, computerized diagnostic systems which use Deep Learning (DL) Convolutional Neural Network (CNN) architectures, are proposed to learn DR patterns from fundus images and identify the severity of the disease. This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature extraction and image classification of DR fundus images. In the proposed model, ResNet50 has shown highest overfitting in comparison to Inception V3, which has shown lowest overfitting when trained using the Kaggle's EyePACS fundus image dataset. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169. EfficientNetB4 has achieved a training accuracy of 99.37% and the highest validation accuracy of 79.11%. DenseNet201 has achieved the highest training accuracy of 99.58% and a validation accuracy of 76.80% which is less than the top-4 best performing models.
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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.