Enhancing fundus image analysis for diabetic retinopathy using CheXNet with CBAM and Grad-CAM visualization
The results indicate that incorporating CBAM improves discriminative feature learning within a DenseNet121-based framework. While the model demonstrates strong performance across two public datasets, further prospective evaluation and external validation are required to assess its clinical applicability in real-world settings.
Authors
- Amro Alhazimi
- Faisal Almarek
- Noor Alqudah (ORCID: https://orcid.org/0000-0001-5341-9137)
- Mohammad Amin
- Wedad Al-Dolat (ORCID: https://orcid.org/0000-0002-7282-2471)
- Salem Alhatamleh
- Aseel Daamseh
- Raghad Malkawi
- Rami Al- Omari
- Rola Madain
- Sarah Husam Aljefri
Institutions
- Jordan University of Science and Technology (JO)
- Al Yarmouk University College (IQ)
- Imam Mohammad ibn Saud Islamic University (SA)
- Yarmouk Private University (SY)
- Islamic University (BD)
- Yarmouk University (JO)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-02-25
- DOI
- https://doi.org/10.3389/fmed.2026.1732109
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Al-Imam Muhammad Ibn Saud Islamic University