BIAN: Bidirectional interwoven attention network for retinal OCT image classification

Retinal diseases are a significant global health concern, requiring advanced diagnostic tools for early detection and treatment. Automated diagnosis of retinal diseases using deep learning can significantly enhance early detection and intervention efforts. However, conventional deep learning models that concentrate on localized perspectives often develop feature representations that lack sufficient semantic discriminative capability. Conversely, models that prioritize global semantic-level information may fail to capture essential, subtle local pathological features. To address this issue, we propose BIAN, a novel Bidirectional Interwoven Attention Network designed for the classification of retinal Optical Coherence Tomography (OCT) images. BIAN synergistically combines the strengths of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) by integrating a ResNet architecture backbone with a ViT backbone through a bidirectional interwoven attention block. This network enables the model to effectively capture both local features and global contextual information. Specifically, the bidirectional interwoven attention block allow the ResNet and ViT components to attend to each other's feature representations, enhancing the network's overall learning capacity. We evaluated BIAN on both the OCTID and OCTDL datasets for retinal disease classification. The OCTID dataset includes conditions such as Age-related Macular Degeneration (AMD), Macular Hole (MH), Central Serous Retinopathy (CSR), etc., while OCTDL covers AMD, Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Vein Occlusion (RVO), etc. On OCTID, the proposed model achieved 95.7% accuracy for five-class classification, outperforming existing state-of-the-art models. On OCTDL, BIAN attained 94.7% accuracy, with consistently high F1-scores (95.6% on OCTID, 94.6% on OCTDL) and AUC values (99.3% and 99.0%, respectively). These results highlight the potential of BIAN as a robust network for retinal OCT image classification in medical applications.