Diabetic Retinopathy Detection Model using Hybrid of U-Net and Vision Transformer Algorithms

Abstract

Diabetic retinopathy is one of the leading causes of vision impairment noticed among individuals with prolonged diabetes. Early-stage detection is very crucial for its treatment. Now, we present a hybrid model which is a combination of U-Net algorithm used for image segmentation and Vision Transformer for classification. The total integration offers a robust model which helps in detecting various stages of diabetic retinopathy. We leverage the use of U-Net algorithm in image segmentation process to delineate the regions of interest in retinal images. Further, the outputs which are segmented are passed into Vision Transformer, which is enhanced by Efficient Net, which is used across various severity levels involved in Diabetic Retinopathy. The usage of transformer architecture helps improve feature extraction and classification performance which ensures that our model captures all patterns in retinal images. We have evaluated our model on APTOS Blindness detection dataset in which our model outperforms traditional convolutional neural networks-based models. Hence, the hybrid approach consisting of combination of both the algorithms demonstrates excellent robustness and generalization which offers a promising application for diabetic retinopathy screening, involving the potential to revolutionize early diagnosis in clinical settings.