ArticleHealthcare informatics research2018
Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images.
Article in Healthcare informatics research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- AI-Based Ocular Age Estimation from Combined OCT and OCTA Metrics: Decade-Stratified Normative Modelling in Healthy Eyes - a Pilot Study.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- 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
- A fast and fully automated system for glaucoma detection using color fundus photographs.Scientific reports · 2023Article
- Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images.American journal of ophthalmology · 2021Article
- Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation.Healthcare informatics research · 2018Article
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Authors and funding
4 authors.
Funding
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Abstract
objectivesGlaucoma is an incurable eye disease and the second leading cause of blindness in the world. Until 2020, the number of patients of this disease is estimated to increase. This paper proposes a glaucoma detection method using statistical features and the k-nearest neighbor algorithm as the classifier.
methodsWe propose three statistical features, namely, the mean, smoothness and 3rd moment, which are extracted from images of the optic nerve head. These three features are obtained through feature extraction followed by feature selection using the correlation feature selection method. To classify those features, we apply the k-nearest neighbor algorithm as a classifier to perform glaucoma detection on fundus images.
resultsTo evaluate the performance of the proposed method, 84 fundus images were used as experimental data consisting of 41 glaucoma image and 43 normal images. The performance of our proposed method was measured in terms of accuracy, and the overall result achieved in this work was 95.24%, respectively.
conclusionsThis research showed that the proposed method using three statistics features achieves good performance for glaucoma detection.
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