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Diabetic Retinopathy (DR), a prevalent and severe complication of diabetes,affects millions of individuals globally, underscoring the need for accurateand timely diagnosis. Recent advancements in imaging technologies, such asUltra-WideField Color Fundus Photography (UWF-CFP) imaging and OpticalCoherence Tomography Angiography (OCTA), provide opportunities for the earlydetection of DR but also pose significant challenges given the disparate natureof the data they produce. This study introduces a novel multimodal approachthat leverages these imaging modalities to notably enhance DR classification.Our approach integrates 2D UWF-CFP images and 3D high-resolution 6x6 mm$^3$OCTA (both structure and flow) images using a fusion of ResNet50 and3D-ResNet50 models, with Squeeze-and-Excitation (SE) blocks to amplify relevantfeatures. Additionally, to increase the model's generalization capabilities, amultimodal extension of Manifold Mixup, applied to concatenated multimodalfeatures, is implemented. Experimental results demonstrate a remarkableenhancement in DR classification performance with the proposed multimodalapproach compared to methods relying on a single modality only. The methodologylaid out in this work holds substantial promise for facilitating more accurate,early detection of DR, potentially improving clinical outcomes for patients.

Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images