Deep Learning-Based DR Screening from Oct Using Layer-Aware Thickness and Texture Maps
Document Type
Conference Proceeding
Source of Publication
Proceedings International Symposium on Biomedical Imaging
Publication Date
4-8-2026
Abstract
Early detection of retinal disease is vital because structural damage can progress without symptoms and lead to irreversible vision loss. Optical coherence tomography (OCT) is well suited to this need since it resolves the retina into discrete layers with micrometer precision. Focusing on diabetic retinopathy (DR), we propose a pipeline that converts OCT layer segmentation into a pixel-aligned thickness image and fuses it with normalized grayscale intensity and local binary pattern (LBP) microtexture as a three-channel input to a compact transformer optimized end to end. The classifier learns complementary evidence from all three channels and, without pretraining, surpasses competitive convolutional neural network (CNN) and transformer baselines with accuracy 99.12% ± 1.99%, specificity 99.33% ± 2.11%, sensitivity 98.95% ± 2.22%, F1 score 99.20% ± 1.78%, and AUC 99.96% ± 0.11%. Ablations confirm that intensity, texture, and morphology contribute in a balanced manner, leading to the observed gains. These results highlight the potential of multimodal, layer-aware OCT modeling to support earlier and more reliable DR screening in routine workflows.
DOI Link
ISBN
[9798331577636]
ISSN
Publisher
IEEE
Volume
2026-April
Disciplines
Computer Sciences | Medicine and Health Sciences
Keywords
diabetic retinopathy (DR), local binary patterns (LBP), Optical coherence tomography (OCT), retinal layer thickness mapping, transformer-based classification
Scopus ID
Recommended Citation
Sakib, Sadman; Elsharkawy, Mohamed; El-Melegy, Moumen; Ali, Asem; Ghazal, Mohammed A.; Taher, Fatma; and El-Baz, Ayman, "Deep Learning-Based DR Screening from Oct Using Layer-Aware Thickness and Texture Maps" (2026). All Works. 8200.
https://zuscholars.zu.ac.ae/works/8200
Indexed in Scopus
yes
Open Access
no