HyReti-Net: hybrid retinal diseases classification and diagnosis network using optical coherence tomography
In this article, a hybrid multi-scale network model integrating dual-branches and a features fusion module was proposed to diagnose retinal diseases. The performance of the proposed method produced promising classification results. On the OCT-2014, OCT-2017 and OCT-C8, experimental results indicated that HyReti-Net achieved better performance than the state-of-the-art networks. This study can provide a reference for clinical diagnosis of ophthalmologists through artificial intelligence technology.
Authors
- Bin Wu (ORCID: https://orcid.org/0000-0003-0413-6987)
- Feng Lu (ORCID: https://orcid.org/0000-0003-0247-6697)
- Kaili Tang (ORCID: https://orcid.org/0000-0002-6137-2487)
- Yuxi Ding
- Zhenbo Zhao
- Liwei Ma (ORCID: https://orcid.org/0000-0002-7867-8060)
- Yuanyuan Lu (ORCID: https://orcid.org/0000-0001-8621-6635)
- Jing Wang (ORCID: https://orcid.org/0000-0001-7751-8607)
- Jikun Yang
- C. T. Hsu
Institutions
- University of Southern California (US)
- Shenyang Aerospace University (CN)
- Anhui Medical University (CN)
- Southern California Clinical and Translational Science Institute (US)
- He Eye Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3389/fmed.2025.1660920
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00