ReviewJournal of healthcare engineering2023
Deep Learning and Medical Image Processing Techniques for Diabetic Retinopathy: A Survey of Applications, Challenges, and Future Trends.
Review in Journal of healthcare engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 45 citations in OpenAlex.
- Advancing Visual Perception Through VCANet-Crossover Osprey Algorithm: Integrating Visual Technologies.Journal of imaging informatics in medicine · 2026Article
- Early Diabetic Retinopathy Detection from OCT Images Using Multifractal Analysis and Multi-Layer Perceptron Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Assessment of Deep Learning Models for Cutaneous Leishmania Parasite Diagnosis Using Microscopic Images.Diagnostics (Basel, Switzerland) · 2023Article
- A multidomain bio-inspired feature extraction and selection model for diabetic retinopathy severity classification: an ensemble learning approach.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a common eye retinal disease that is widely spread all over the world. It leads to the complete loss of vision based on the level of severity. It damages both retinal blood vessels and the eye's microscopic interior layers. To avoid such issues, early detection of DR is essential in association with routine screening methods to discover mild causes in manual initiation. But these diagnostic procedures are extremely difficult and expensive. The unique contributions of the study include the following: first, providing detailed background of the DR disease and the traditional detection techniques. Second, the various imaging techniques and deep learning applications in DR are presented. Third, the different use cases and real-life scenarios are explored relevant to DR detection wherein deep learning techniques have been implemented. The study finally highlights the potential research opportunities for researchers to explore and deliver effective performance results in diabetic retinopathy detection.
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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.