| Sumario: | Early detection of Diabetic Retinopathy (DR) is vital for preserving vision and preventing deterioration of eyesight. Fundus Fluorescein Angiography (FFA), recognized as the gold standard for diagnosing DR, effectively reveals abnormalities in retinal vasculature. Given the labor-intensive and costly nature of manual DR diagnosis, along with its low accuracy, developing a DR classification model based on FFA using deep learning techniques is crucial. Furthermore, DR classification faces challenges such as minimal lesion variance between different disease stages and significant size variations of lesions within the same stage, with small lesions often overlooked by existing models. We propose a deep learning model, D-GET, utilizing a Group-Enhanced Transformer for classifying DR lesion severity in FFA images. The D-GET model incorporates a Full-Scale Transformer Block, where the Group-Focal module captures feature information at multiple scales, from fine details to broader patterns, and adaptively integrates contextual information, enhancing the model's ability to detect small-scale lesions. The model also includes a Channel Adaptive Attention Module (CAAM) that synthesizes channel and spatial information to improve feature detection and localization. Experimental findings indicate that the D-GET method we developed surpasses existing methods on a custom dataset. The D-GET model, developed for DR classification using FFA images, significantly improves the detection of small-scale lesions. This advancement enhances the diagnosis and treatment of DR, establishing a solid foundation for its broader application across various domains of ophthalmic and general medical imaging.
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