ArticleData in brief2021
Dataset from fundus images for the study of diabetic retinopathy.
Article in Data in brief, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- A Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-Attention Model Architecture for Precise Medical Image Analysis and Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics.Sensors (Basel, Switzerland) · 2025Article
- Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.Nature communications · 2025Article
- An inherently interpretable AI model improves screening speed and accuracy for early diabetic retinopathy.PLOS digital health · 2025Article
- Development and validation of an AI algorithm to generate realistic and meaningful counterfactuals for retinal imaging based on diffusion models.PLOS digital health · 2025Article
- A dataset of color fundus images for the detection and classification of eye diseases.Data in brief · 2024Article
- Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy.Data in brief · 2024Article
- Dataset of fundus images for the diagnosis of ocular toxoplasmosis.Data in brief · 2023Article
- A Global Review of Publicly Available Datasets Containing Fundus Images: Characteristics, Barriers to Access, Usability, and Generalizability.Journal of clinical medicine · 2023Article
- Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2022Article
- Automated image curation in diabetic retinopathy screening using deep learning.Scientific reports · 2022Article
- Value of Combining Optical Coherence Tomography with Fundus Photography in Screening Retinopathy in Patients with High Myopia.Journal of healthcare engineering · 2022Article
- Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.Journal of imaging · 2021Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article presents a database containing 757 color fundus images acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas (FCM), Universidad Nacional de Asunción (UNA), Paraguay. Firstly, the retinal images were acquired with a clinical procedure presented in this paper. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand. Next, two expert ophthalmologists have classified the dataset. These data can help physicians and researchers in the detection of cases of Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), in their different stages. The dataset generated will be useful for ophthalmologists and researchers to work on automatic detection algorithms for Diabetic Retinopathy (DR).
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Registered trials
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