Search in:

Current Eye Research Volume 46, 2021 - Issue 10

Submit an article Journal homepage

Open access

3,174

Views

CrossRef citations to date

Altmetric

Glaucoma

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks

Hyeonsung Choa Intelligence and Robot System Research Group, Electronics & Telecommunication Research Institute, Daejeon, Republic of KoreaView further author information

Young Hoon Hwangb Department of Ophthalmology, Chungnam National University Hospital, Daejeon, Republic of KoreaView further author information

Jae Keun Chungb Department of Ophthalmology, Chungnam National University Hospital, Daejeon, Republic of Korea

https://orcid.org/0000-0003-2968-834X View further author information

Kwan Bok Leeb Department of Ophthalmology, Chungnam National University Hospital, Daejeon, Republic of KoreaView further author information

Ji Sang Parka Intelligence and Robot System Research Group, Electronics & Telecommunication Research Institute, Daejeon, Republic of KoreaView further author information

Hong-Gee Kimc Biomedical Knowledge Engineering Laboratory, Seoul National University, Seoul, Republic of KoreaView further author information

Jae Hoon Jeongd Department of Ophthalmology, Konyang University Hospital, Konyag University College of Medicine, Daejeon, Republic of KoreaCorrespondence[email protected]

https://orcid.org/0000-0001-7311-7080 View further author information

show all

Pages 1516-1524 | Received 19 Jun 2020, Accepted 21 Feb 2021, Published online: 06 Apr 2021

Cite this article
https://doi.org/10.1080/02713683.2021.1900268
CrossMark

Full Article
Figures & data
References
Supplemental
Citations
Metrics
Licensing
Reprints & Permissions
View PDF PDF View EPUB EPUB

Citations (19)

Keep up to date with the latest research on this topic with citation updates for this article.

Subscribe to citation updates

Articles from other publishers (19)

Mo’ath AlShawabkeh, Saif Aldeen AlRyalat, Muawyah Al Bdour, Ayat Alni’mat & Mousa Al-Akhras. (2024) The utilization of artificial intelligence in glaucoma: diagnosis versus screening. Frontiers in Ophthalmology 4.
Crossref

Rajeshwar Patil & Sanjeev Sharma. (2024) Automatic glaucoma detection from fundus images using transfer learning. Multimedia Tools and Applications.
Crossref

Xiaoqin Huang, Md Rafiqul Islam, Shanjita Akter, Fuad Ahmed, Ehsan Kazami, Hashem Abu Serhan, Alaa Abd-alrazaq & Siamak Yousefi. (2023) Artificial intelligence in glaucoma: opportunities, challenges, and future directions. BioMedical Engineering OnLine 22:1.
Crossref

Homa Rashidisabet, Abhishek Sethi, Ponpawee Jindarak, James Edmonds, R. V. Paul Chan, Yannek I. Leiderman, Thasarat Sutabutr Vajaranant & Darvin Yi. (2023) Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data. Translational Vision Science & Technology 12:11, pages 8.
Crossref

Jing-Zhe Wang, Nan-Han Lu, Wei-Chang Du, Kuo-Ying Liu, Shih-Yen Hsu, Chi-Yuan Wang, Yun-Ju Chen, Li-Ching Chang, Wen-Hung Twan, Tai-Been Chen & Yung-Hui Huang. (2023) Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models. Healthcare 11:15, pages 2228.
Crossref

Veronika Kurilová, Szabolcs Rajcsányi, Zuzana Rábeková, Jarmila Pavlovičová, Miloš Oravec & Nora Majtánová. (2023) Detecting glaucoma from fundus images using ensemble learning. Journal of Electrical Engineering 74:4, pages 328-335.
Crossref

Riley Kiefer, Muhammad Abid, Mahsa Raeisi Ardali, Jessica Steen & Ehsan Amjadian. (2023) Automated Fundus Image Standardization Using a Dynamic Global Foreground Threshold Algorithm. Automated Fundus Image Standardization Using a Dynamic Global Foreground Threshold Algorithm.

Mohammad Zedan, Mohd Zulkifley, Ahmad Ibrahim, Asraf Moubark, Nor Kamari & Siti Abdani. (2023) Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review. Diagnostics 13:13, pages 2180.
Crossref

Linyu Zhang, Li Tang, Min Xia & Guofan Cao. (2023) The application of artificial intelligence in glaucoma diagnosis and prediction. Frontiers in Cell and Developmental Biology 11.
Crossref

Poonguzhali Elangovan, D. Vijayalakshmi & Malaya Kumar Nath. 2023. Computational Methods and Deep Learning for Ophthalmology. Computational Methods and Deep Learning for Ophthalmology 89 106 .

Özcan YILDIRIM & Feyza ALTUNBEY ÖZBAY. (2022) Fundus Görüntülerinden Derin Öğrenme Teknikleri ile Glokom Hastalığının TespitiDetection of Glaucoma Disease with Deep Learning Techniques from Fundus Images. European Journal of Science and Technology.
Crossref

Ming-Tse Kuo, Benny Wei-Yun Hsu, Yi Sheng Lin, Po-Chiung Fang, Hun-Ju Yu, Yu-Ting Hsiao & Vincent S. Tseng. (2022) Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis. Diagnostics 12:12, pages 2948.
Crossref

Poonguzhali Elangovan & Malaya Kumar Nath. (2022) En‐ConvNet : A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks . International Journal of Imaging Systems and Technology 32:6, pages 2034-2048.
Crossref

Qaisar Abbas, Imran Qureshi, Junhua Yan & Kashif Shaheed. (2022) Machine Learning Methods for Diagnosis of Eye-Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities. Archives of Computational Methods in Engineering 29:6, pages 3861-3918.
Crossref

Jayanthi Ganapathy, Purushothaman R.Sathishkumar M.Vishal L.. 2022. Machine Learning and AI Techniques in Interactive Medical Image Analysis. Machine Learning and AI Techniques in Interactive Medical Image Analysis 51 69 .

Zhenwei Li, Mengying Xu, Xiaoli Yang & Yanqi Han. (2022) Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion. Micromachines 13:6, pages 947.
Crossref

Jayanthi Ganapathy, Karan Vijaykumar, Sathishkumar Sundar, Thiruvengadam Sadasivam, Rajalakshmanan Chandrasekaran & Vishal Lakshmanan. (2022) Evaluation of Deep Learning Framework for Detection and Diagnosis of Glaucoma. Evaluation of Deep Learning Framework for Detection and Diagnosis of Glaucoma.

Cansu Yuksel Elgin, Dinah Chen & Lama A. Al‐Aswad. (2022) Ophthalmic imaging for the diagnosis and monitoring of glaucoma: A review. Clinical & Experimental Ophthalmology 50:2, pages 183-197.
Crossref

R. Bunod, E. Augstburger, E. Brasnu, A. Labbe & C. Baudouin. (2022) Intelligence artificielle et glaucome : une revue de la littérature. Journal Français d'Ophtalmologie 45:2, pages 216-232.
Crossref

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.

People also read
Recommended articles
Cited by

To cite this article:

Reference style: APA Chicago Harvard

Citation copied to clipboard

Reference styles above use APA (6th edition), Chicago (16th edition) & Harvard (10th edition)

Download citation

Download a citation file in RIS format that can be imported by citation management software including EndNote, ProCite, RefWorks and Reference Manager.

Choose format: RIS BibTex RefWorks Direct Export

Choose options: Citation Citation & abstract Citation & references

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks

Articles from other publishers (19)

Information for

Open access

Opportunities

Help and information

Your download is now in progress and you may close this window

Login or register to access this feature

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks

Citations (19)

Articles from other publishers (19)

Related research

To cite this article:

Download citation

Information for

Open access

Opportunities

Help and information

Keep up to date