Document Type
Article
Source of Publication
IEEE Access
Publication Date
4-17-2025
Abstract
Age-related macular degeneration (AMD) is a prevalent retinal disorder in the elderly, often leading to significant vision impairment. The diagnosis of AMD is confirmed through various medical imaging modalities, with color fundus photography (CFP) being a primary tool. The detection and staging of AMD-severity depend on several factors, including the number and size of drusen, the presence of pigmentary changes, geographic atrophy, and neovascularization, all of which are identifiable through CFP. In this study, we introduce an innovative dual-vision transformer-based network designed to automatically detect AMD and classify its severity into either dry AMD or wet AMD using CFP. Early diagnosis and accurate staging of AMD are crucial in mitigating the progression of the disease, making this work particularly valuable. Our proposed model, Seg-Swin, leverages a dual attention-based transformer network architecture, comprising two key stages. The first stage employs the SegFormer transformer model for the precise detection of AMD-related lesions, while the second stage utilizes the Swin transformer model to classify the detected lesions into dry or wet AMD. Our extensive experimental results demonstrate that the Seg-Swin model outperforms existing approaches, achieving remarkable diagnostic accuracy with metrics such as 98.7% accuracy, 99% sensitivity, 97.95% F1-score, and 98.24% specificity. By combining the strengths of advanced transformer models in both identification and classification tasks, the Seg-Swin model offers a comprehensive and powerful solution for detecting and staging AMD. The integration of these dual attention mechanisms allows the model to more precisely interpret complex retinal images, which is crucial for early diagnosis and accurate staging of AMD.
DOI Link
ISSN
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Disciplines
Computer Sciences
Keywords
AMD classification, dry AMD, fundus photography, lesion detection, neovascular AMD, SegFormer, Swin transformer
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
El-Den, Niveen Nasr; Elsharkawy, Mohamed; Saleh, Ibrahim; Mahmoud, Ali H.; Ghazal, Mohammed; Khalil, Ashraf; Sewelam, Ashraf; Mahdi, Hani; and El-Baz, Ayman, "Seg-Swin: A Dual-Attention Transformer Model for Advanced AMD Classification and Lesion Detection Using Color Fundus Imaging" (2025). All Works. 7217.
https://zuscholars.zu.ac.ae/works/7217
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series