A Hybrid Transformers-based Convolutional Neural Network Model for Keratoconus Detection in Scheimpflug-based Dynamic Corneal Deformation Videos
The hybrid Transformer-based model was highly sensitive and specific in discriminating normal from keratoconic eyes using DCDV(s) at levels that may prove useful in clinical practice.
Authors
- Rossen Hazarbasanov
- Suphi Taneri (ORCID: https://orcid.org/0000-0003-0593-6847)
- M. Hossein Nowroozzadeh (ORCID: https://orcid.org/0000-0002-7412-1900)
- Hidenori Takahashi (ORCID: https://orcid.org/0000-0001-5331-4730)
- Siamak Yousefi (ORCID: https://orcid.org/0000-0001-8633-5730)
- Ramin Salouti (ORCID: https://orcid.org/0000-0002-5853-4799)
- Hazem Abdelmotaal (ORCID: https://orcid.org/0000-0002-7429-3796)
- Alexandru Lavric (ORCID: https://orcid.org/0000-0001-7734-4854)
- Ali H. Al‐Timemy (ORCID: https://orcid.org/0000-0003-2738-8896)
Institutions
- Ştefan cel Mare University of Suceava (RO)
- University of Baghdad (IQ)
- Jichi Medical University (JP)
- University of Tennessee Health Science Center (US)
- Shiraz University of Medical Sciences (IR)
- Instituto de Olhos de Goiânia (BR)
- Universidade Federal de São Paulo (BR)
- Ruhr University Bochum (DE)
- Assiut University (EG)
Publication Details
- Journal
- Journal of Ophthalmic and Vision Research
- Published
- 2025-06-18
- DOI
- https://doi.org/10.18502/jovr.v20.17716
- Citations
- 1
- Primary Topic
- Corneal surgery and disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.36