OpthaNet: Attention‐Integrated Architecture for High‐Precision Multi‐Class Ophthalmic Image Classification

This study investigated the efficacy of pre-trained deep learning models for multi-class classification of eye diseases, namely cataract, diabetic retinopathy, and glaucoma, using fundus images. Although CNN and transformer-based models have been extensively explored separately in ophthalmic diagnostics, a direct comparative analysis remains limited. Moreover, recent high-performing systems frequently rely on heavy backbones, ensembles, or large-scale domain pretraining, which can be impractical for resource-constrained screening pipelines. We evaluated three models, EfficientNetB3, MobileNetV2 and vision Transformer, with tailored modifications. An attention-enhanced feature refinement module and the OpthaHead custom classifier enhanced EfficientNetB3 and MobileNetV2, while META customization optimized vision Transformer. The proposed design explicitly targets two practical bottlenecks observed in ophthalmic transfer learning, insufficient feature selectivity for subtle lesions and structural regions,…