ArticleMedical & biological engineering & computing2019
A Random Forest classifier-based approach in the detection of abnormalities in the retina.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.Journal of medical Internet research · 2021Pooled it
- A comprehensive review on automated diabetic retinopathy detection and classification using fundus image.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Detection of referable diabetic retinopathy using machine learning on routine clinical data.Frontiers in medicine · 2026Article
- 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
- Development and Evaluation of a Deep Learning Algorithm to Differentiate Between Membranes Attached to the Optic Disc on Ultrasonography.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification.Briefings in bioinformatics · 2024Article
- Association of subretinal drusenoid deposits and cardiovascular disease.Scientific reports · 2024Article
- Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2024Article
- Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.Revista brasileira de epidemiologia = Brazilian journal of epidemiology · 2024Article
- VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction.Journal of imaging · 2023Article
- Construction and evaluation of in-house methylation-sensitive SNaPshot system and three classification prediction models for identifying the tissue origin of body fluid.Journal of Zhejiang University. Science. B · 2023Article
- Fine-mapping of retinal vascular complexity loci identifies Notch regulation as a shared mechanism with myocardial infarction outcomes.Communications biology · 2023Article
- Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.Diagnostics (Basel, Switzerland) · 2023Review
- Innovation in Hyperinsulinemia Diagnostics with ANN-L(Diagnostics (Basel, Switzerland) · 2023Article
- Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model.Frontiers in physiology · 2023Article
- Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review.Multimedia tools and applications · 2023Article
- Automated method for real-time AMD screening of fundus images dedicated for mobile devices.Medical & biological engineering & computing · 2022Article
- Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.IEEE journal of translational engineering in health and medicine · 2022Article
- Random forest-based prediction of stroke outcome.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases. A computer-aided diagnosis system is proposed for detecting retina abnormalities, which combines K means-based segmentation of the retina image, after due preprocessing, followed by machine learning techniques, using several low level and statistical features. Abnormalities in the retina that are classified are caused by age-related macular degeneration and diabetic retinopathy. Performance measures used in the analysis are accuracy, sensitivity, specificity, F-measure, and Mathew correlation coefficient. A comparison with another machine learning technique, the Naïve Bayes classifier shows that the classification achieved by Random Forest classifier is 93.58% and it outperforms Naïve Bayes classifier which yields an accuracy of 83.63%. Graphical abstract Random Forest classifier for abnormality detection in retina images.
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.