ArticleHealthcare (Basel, Switzerland)2022
Deep Learning for the Detection and Classification of Diabetic Retinopathy with an Improved Activation Function.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- Enhanced deep learning model for prediction of diabetic mellitus on optical coherence tomography angiography images.Quantitative imaging in medicine and surgery · 2026Article
- Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.Ophthalmology and therapy · 2026Review
- Hybrid quantum-classical deep learning framework for balanced multiclass diabetic retinopathy classification.MethodsX · 2025Article
- A Multi-Model Image Enhancement and Tailored U-Net Architecture for Robust Diabetic Retinopathy Grading.Diagnostics (Basel, Switzerland) · 2025Article
- Diabetic retinopathy screening using machine learning: a systematic review.BMC biomedical engineering · 2025Article
- QuantumNet: An enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning.MethodsX · 2025Article
- A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing.Scientific reports · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- A machine learning-based prediction of diabetic retinopathy using the Korea national health and nutrition examination survey (2008-2012, 2017-2021).Frontiers in medicine · 2025Article
- A lightweight multi-deep learning framework for accurate diabetic retinopathy detection and multi-level severity identification.Frontiers in medicine · 2025Article
- Article
- Evaluation of Structural Retinal Layer Alterations in Retinitis Pigmentosa.Romanian journal of ophthalmologyArticle
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is an eye disease triggered due to diabetes, which may lead to blindness. To prevent diabetic patients from becoming blind, early diagnosis and accurate detection of DR are vital. Deep learning models, such as convolutional neural networks (CNNs), are largely used in DR detection through the classification of blood vessel pixels from the remaining pixels. In this paper, an improved activation function was proposed for diagnosing DR from fundus images that automatically reduces loss and processing time. The DIARETDB0, DRIVE, CHASE, and Kaggle datasets were used to train and test the enhanced activation function in the different CNN models. The ResNet-152 model has the highest accuracy of 99.41% with the Kaggle dataset. This enhanced activation function is suitable for DR diagnosis from retinal fundus images.
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