ArticleHealthcare (Basel, Switzerland)2023
Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis.Frontiers in endocrinology · 2025Pooled it
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- A Deep Learning-Based Method for Non-Destructive Estimation of Carbonate Carbon Storage in Biogenic Shells on Marine Engineering Materials.Materials (Basel, Switzerland) · 2026Article
- Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal integration.Frontiers in medicine · 2026Review
- Comparative analysis of novel preprocessing techniques and deep learning based multi modal feature fusion for diabetic retinopathy grading.Scientific reports · 2025Article
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- From Detection to Diagnosis: An Advanced Transfer Learning Pipeline Using YOLO11 with Morphological Post-Processing for Brain Tumor Analysis for MRI Images.Journal of imaging · 2025Article
- Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Automated high precision PCOS detection through a segment anything model on super resolution ultrasound ovary images.Scientific reports · 2025Article
- The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.Scientific reports · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- Automated Brain Tumor segmentation using hybrid YOLO and SAM.Current medical imaging · 2025Article
- Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection.Brain sciences · 2024Article
- Article
- Enhancement of Diabetic Retinopathy Prognostication Using Deep Learning, CLAHE, and ESRGAN.Diagnostics (Basel, Switzerland) · 2023Article
- YOLOv5-FPN: A Robust Framework for Multi-Sized Cell Counting in Fluorescence Images.Diagnostics (Basel, Switzerland) · 2023Article
- Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP.Diagnostics (Basel, Switzerland) · 2023Article
- Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy.Ophthalmology scienceArticle
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.
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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.