ArticleHealth information science and systems2020
Automated detection of mild and multi-class diabetic eye diseases using deep learning.
Article in Health information science and systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 176 citations in OpenAlex.
- Beyond the pelvic ring: benchmarking multi-bone segmentation for ROI-Guided fracture detection.Scientific reports · 2026Article
- A Hierarchical Deep Learning Architecture for Diagnosing Retinal Diseases Using Cross-Modal OCT to Fundus Translation in the Lack of Paired Data.Journal of imaging · 2026Article
- Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.Current medical imaging · 2025Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification.Frontiers in oncology · 2025Article
- A multi-source heterogeneous medical data enhancement framework based on lakehouse.Health information science and systems · 2024Article
- Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning.Health information science and systems · 2024Article
- A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services.Health information science and systems · 2023Article
- BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification.Health information science and systems · 2023Article
- Using nutrigenomics to guide personalized nutrition supplementation for bolstering immune system.Health information science and systems · 2023Article
- The nnU-Net based method for automatic segmenting fetal brain tissues.Health information science and systems · 2023Article
- Efficient novel network and index for alcoholism detection from EEGs.Health information science and systems · 2023Article
- Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm.Health information science and systems · 2023Article
- CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection.Health information science and systems · 2023Article
- Deep-Ocular: Improved Transfer Learning Architecture Using Self-Attention and Dense Layers for Recognition of Ocular Diseases.Diagnostics (Basel, Switzerland) · 2023Article
- Identifying informative tweets during a pandemic via a topic-aware neural language model.World wide web · 2023Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Automatic breast lesion segmentation in phase preserved DCE-MRIs.Health information science and systems · 2022Article
- A multi-label classification system for anomaly classification in electrocardiogram.Health information science and systems · 2022Article
- A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration.Health information science and systems · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic eye disease is a collection of ocular problems that affect patients with diabetes. Thus, timely screening enhances the chances of timely treatment and prevents permanent vision impairment. Retinal fundus images are a useful resource to diagnose retinal complications for ophthalmologists. However, manual detection can be laborious and time-consuming. Therefore, developing an automated diagnose system reduces the time and workload for ophthalmologists. Recently, the image classification using Deep Learning (DL) in between healthy or diseased retinal fundus image classification already achieved a state of the art performance. While the classification of mild and multi-class diseases remains an open challenge, therefore, this research aimed to build an automated classification system considering two scenarios: (i) mild multi-class diabetic eye disease (DED), and (ii) multi-class DED. Our model tested on various datasets, annotated by an opthalmologist. The experiment conducted employing the top two pretrained convolutional neural network (CNN) models on ImageNet. Furthermore, various performance improvement techniques were employed, i.e.,
Indexed as
Identifiers
What OpenQuestion holds
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.