ArticleComputational and mathematical methods in medicine2021
A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 31 citations in OpenAlex.
- Evaluating the strengths and limitations of multimodal ChatGPT-4 in detecting glaucoma using fundus images.Frontiers in ophthalmology · 2024Article
- Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine.Computational and mathematical methods in medicine · 2022Article
- Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images.Computational intelligence and neuroscience · 2022Article
- Prediction of the Age and Gender Based on Human Face Images Based on Deep Learning Algorithm.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.
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