SynthesisBiomedical engineering online2020
Machine learning applied to retinal image processing for glaucoma detection: review and perspective.
Synthesis in Biomedical engineering online, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 135 citations in OpenAlex.
- Computational single fundus image restoration techniques: a review.Frontiers in ophthalmology · 2024Pooled it
- Accuracy of Using Generative Adversarial Networks for Glaucoma Detection: Systematic Review and Bibliometric Analysis.Journal of medical Internet research · 2021Pooled it
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Beyond Accuracy: A Controlled Comparative Glaucoma Screening Benchmark Across Deep Learning and Hybrid Models Under Within- and Cross-Dataset Conditions.Journal of clinical medicine · 2026Article
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach.PLoS computational biology · 2026Article
- Prediction of primary glaucoma: development and validation of multiple machine learning models.Scientific reports · 2026Article
- SwinCup-DiscNet: A fusion transformer framework for glaucoma diagnosis using optic disc and cup features.Scientific reports · 2026Article
- Leveraging fundus images for on device eye disease diagnosis with AI powered lightweight software hardware framework.Scientific reports · 2025Article
- Explainable Transformer-Based Framework for Glaucoma Detection from Fundus Images Using Multi-Backbone Segmentation and vCDR-Based Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.Investigative ophthalmology & visual science · 2025Article
- Article
- Microvascular Metrics on Diabetic Retinopathy Severity: Analysis of Diabetic Eye Images from Real-World Data.Biomedicines · 2024Article
- Robust PCA withJournal of imaging · 2024Article
- Enhancing Medical Image Classification with an Advanced Feature Selection Algorithm: A Novel Approach to Improving the Cuckoo Search Algorithm by Incorporating Caputo Fractional Order.Diagnostics (Basel, Switzerland) · 2024Article
- Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection.Scientific reports · 2024Article
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Article
- Developments in Image Processing Using Deep Learning and Reinforcement Learning.Journal of imaging · 2023Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection. ML has proven to be a significant tool for the development of computer aided technology. Furthermore, secondary research has been widely conducted over the years for ophthalmologists. Such aspects indicate the importance of ML in the context of retinal image processing.
methodsThe publications that were chosen to compose this review were gathered from Scopus, PubMed, IEEEXplore and Science Direct databases. Then, the papers published between 2014 and 2019 were selected . Researches that used the segmented optic disc method were excluded. Moreover, only the methods which applied the classification process were considered. The systematic analysis was performed in such studies and, thereupon, the results were summarized. DISCUSSION: Based on architectures used for ML in retinal image processing, some studies applied feature extraction and dimensionality reduction to detect and isolate important parts of the analyzed image. Differently, other works utilized a deep convolutional network. Based on the evaluated researches, the main difference between the architectures is the number of images demanded for processing and the high computational cost required to use deep learning techniques.
conclusionsAll the analyzed publications indicated it was possible to develop an automated system for glaucoma diagnosis. The disease severity and its high occurrence rates justify the researches which have been carried out. Recent computational techniques, such as deep learning, have shown to be promising technologies in fundus imaging. Although such a technique requires an extensive database and high computational costs, the studies show that the data augmentation and transfer learning techniques have been applied as an alternative way to optimize and reduce networks training.
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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.