ArticleMedical & biological engineering & computing2020
Automated glaucoma screening method based on image segmentation and feature extraction.
Article in Medical & biological engineering & computing, 2020. 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, 53 citations in OpenAlex.
- A hybrid framework for glaucoma detection through federated machine learning and deep learning models.BMC medical informatics and decision making · 2024Article
- Clinical Perspectives on the Use of Computer Vision in Glaucoma Screening.Medicina (Kaunas, Lithuania) · 2024Review
- Identifying the Edges of the Optic Cup and the Optic Disc in Glaucoma Patients by Segmentation.Sensors (Basel, Switzerland) · 2023Article
- Intelligent Localization Sampling System Based on Deep Learning and Image Processing Technology.Sensors (Basel, Switzerland) · 2022Article
- Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features.BMC medical imaging · 2022Article
- A Multimodal Classification Architecture for the Severity Diagnosis of Glaucoma Based on Deep Learning.Frontiers in neuroscience · 2022Article
- Glaucoma Detection Using Image Processing and Supervised Learning for Classification.Journal of healthcare engineering · 2022Article
- An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus.Medical & biological engineering & computing · 2021Article
- Measurement of the Inner Macular Layers for Monitoring of Glaucoma: Confounding Effects of Age-Related Macular Degeneration.Ophthalmology. GlaucomaObservational
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Glaucoma is a chronic disease that threatens eye health and can cause permanent blindness. Since there is no cure for glaucoma, early screening and detection are crucial for the prevention of glaucoma. Therefore, a novel method for automatic glaucoma screening that combines clinical measurement features with image-based features is proposed in this paper. To accurately extract clinical measurement features, an improved UNet++ neural network is proposed to segment the optic disc and optic cup based on region of interest (ROI) simultaneously. Some important clinical measurement features, such as optic cup to disc ratio, are extracted from the segmentation results. Then, the increasing field of view (IFOV) feature model is proposed to fully extract texture features, statistical features, and other hidden image-based features. Next, we select the best feature combination from all the features and use the adaptive synthetic sampling approach to alleviate the uneven distribution of training data. Finally, a gradient boosting decision tree (GBDT) classifier for glaucoma screening is trained. Experimental results based on the ORIGA dataset show that the proposed algorithm achieves excellent glaucoma screening performance with sensitivity of 0.894, accuracy of 0.843, and AUC of 0.901, which is superior to other existing methods.Graphical abstract Framework of the proposed glaucoma classification method.
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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.