Realtime Retinopathy Detection via a Mobile Fundus Camera

Abstract

Diabetic Retinopathy (DR) is a frequent complication of diabetes mellitus that compromises retinal function in more than 50% of type 2 diabetic patients. It occurs when the retina’s blood vessels deteriorate. These altered vessels can dilate, leak fluid (plasma, lipids, and/or blood), and even clog, leaving part of the retina without blood flow. All these phenomena that occur as a result of diabetes can cause progressive damage to the structures of the eyeball, leading to a severe reduction in vision and even, without appropriate treatment, to blindness in the working age. In our work, we propose a framework for diabetic retinopathy detection based on retinal lesions using advanced deep learning. We use the U-Mamba architecture for retinal and blood vessel segmentation, achieving high F1 scores for various lesion types. Our Swin Transformer-based classification model, incorporating lesion segmentation masks, demonstrated exceptional performance across multiple datasets, with up to 97.75% accuracy on the EyePACS dataset. This approach outperformed existing models across various datasets, showing promise for clinical DR diagnosis.