TL-MED: Multiclass eye disease classification based on ensemble transfer learning and CRVO–BRVO detection via a single shot multibox detector
The proposed AI-driven approaches provide a reliable solution for the early and precise detection of common eye diseases. By leveraging advanced deep learning techniques, our work contributes to the medical field by assisting healthcare professionals in diagnosis and paving the way for improved diagnostic tools and enhanced patient care.
Authors
- Md. Rasel Uddin (ORCID: https://orcid.org/0000-0001-5811-9512)
- Md. Alamin Talukder (ORCID: https://orcid.org/0000-0002-3192-1000)
- Md Ashraful Islam (ORCID: https://orcid.org/0000-0002-9491-3876)
- Ferdaus Anam Jibon (ORCID: https://orcid.org/0009-0004-5968-9903)
- Ansam Khraisat (ORCID: https://orcid.org/0000-0002-8623-0987)
- Mohsin Kazi (ORCID: https://orcid.org/0000-0002-5611-0378)
- Fazle Rabby
- Md. Ashraf Uddin (ORCID: https://orcid.org/0000-0002-4316-4975)
- Fazle Rabbi Rushu
- Naimur Rahman
Institutions
- Crown College (US)
- University of Information Technology and Sciences (BD)
- Deakin University (AU)
- King Saud University (SA)
- International University of Business Agriculture and Technology (BD)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251379729
- Citations
- 1
- Primary Topic
- Retinal and Optic Conditions
- Type
- article
- Field-Weighted Citation Impact
- 1.71