Deep Learning for Multi-Disease OCT Classification in Clinical Retina Practice Using Attention Mechanisms and...

Artificial intelligence (AI) is increasingly used in ophthalmic imaging to support early detection of vision-threatening retinal diseases such as age-related macular degeneration, diabetic retinopathy (DR) and choroidal neovascularisation (CNV). However, optical coherence tomography (OCT)-based deep learning models may remain sensitive to image degradation, struggle to resolve subtle inter-class morphological differences and show reduced reliability beyond controlled benchmark settings. We propose AESNet-SEC, a lightweight attention-enhanced SqueezeNet framework integrating multi-scale dilated convolutions, spatial attention, squeeze-and-excitation recalibration and contrastive representation learning. The framework was evaluated on the Retinal OCT-C8 dataset (24,000 images; eight classes) and a prospectively collected clinical dataset from Zeyaei Hospital, Iran (9732 OCT B-scans; four classes). AESNet-SEC achieved 98.56% accuracy on OCT-C8 and 96.33%-96.98% across four repeated clinical evaluations. Ablation and ROC analyses showed the strongest overall discrimination for the complete configuration, while controlled Gaussian-noise testing revealed greater degradation for DR and drusen. Grad-CAM visualisations highlighted anatomically plausible retinal regions, with independent retinal-specialist review providing qualitative support for the observed activation patterns. Overall, AESNet-SEC combines strong multi-class discrimination, computational efficiency, controlled robustness and visual interpretability, while broader patient-disjoint, multi-centre validation remains necessary.