ArticleMedical & biological engineering & computing2021
An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus.
Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 70 citations in OpenAlex.
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.NPJ digital medicine · 2025Article
- Robust PCA withJournal of imaging · 2024Article
- Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets.Medical & biological engineering & computing · 2023Article
- Detection of Glaucoma on Fundus Images Using Deep Learning on a New Image Set Obtained with a Smartphone and Handheld Ophthalmoscope.Healthcare (Basel, Switzerland) · 2022Article
- An Accelerated Failure Time Cure Model with Shifted Gamma Frailty and Its Application to Epidemiological Research.Healthcare (Basel, Switzerland) · 2022Article
- Review of Machine Learning Applications Using Retinal Fundus Images.Diagnostics (Basel, Switzerland) · 2022Review
- A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning.Frontiers in neuroscience · 2022Article
- Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images.Multimedia tools and applications · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes a deep image analysis-based model for glaucoma diagnosis that uses several features to detect the formation of glaucoma in retinal fundus. These features are combined with most extracted parameters like inferior, superior, nasal, and temporal region area, and cup-to-disc ratio that overall forms a deep image analysis. This proposed model is exercised to investigate the various aspects related to the prediction of glaucoma in retinal fundus images that help the ophthalmologist in making better decisions for the human eye. The proposed model is presented with the combination of four machine learning algorithms that provide the classification accuracy of 98.60% while other existing models like support vector machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes provide individually with accuracies of 97.61%, 90.47%, and 95.23% respectively. These results clearly demonstrate that this proposed model offers the best methodology to an early diagnosis of glaucoma in retinal fundus.
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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.