Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture
In this article, an ensemble deep learning was proposed to classify dry AMD progression stages. The performance of the proposed architecture produced promising classification results which showed its advantage to provide global diagnosis for early AMD screening. The classification performance demonstrated its potential for individualized treatment plans for patients with AMD.
Authors
- Jikun Yang (ORCID: https://orcid.org/0000-0002-1236-3920)
- Feng Lü (ORCID: https://orcid.org/0000-0002-6620-2399)
- Bin Wu (ORCID: https://orcid.org/0000-0003-2755-6706)
- Liwei Ma (ORCID: https://orcid.org/0000-0001-5018-8388)
- Kaili Tang (ORCID: https://orcid.org/0000-0002-6137-2487)
- Yuxi Ding
- Zhenbo Zhao
- Yuanyuan Lu (ORCID: https://orcid.org/0000-0002-2050-5167)
- Jing Wang (ORCID: https://orcid.org/0000-0001-7751-8607)
Institutions
- Shenyang Aerospace University (CN)
- Anhui Medical University (CN)
- He Eye Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-10-09
- DOI
- https://doi.org/10.3389/fmed.2024.1438768
- Citations
- 6
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.43