ResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images.
Journal:
Computers in biology and medicine
PMID:
39823821
Abstract
BACKGROUND AND OBJECTIVE: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburdened healthcare systems. This highlights the need for automated, accurate, and personalized machine learning approaches for early DR detection and treatment. Although several deep learning models have been widely used for DR diagnosis, Vision Transformers have recently demonstrated superior image analysis capabilities by capturing long-range dependencies. A hybrid model named ResViT FusionNet has been proposed to improve the accuracy of DR detection in this work.