  Diabetic Retinopathy (DR) is considered one of the significant concerns
worldwide, primarily due to its impact on causing vision loss among most people
with diabetes. The severity of DR is typically comprehended manually by
ophthalmologists from fundus photography-based retina images. This paper deals
with an automated understanding of the severity stages of DR. In the
literature, researchers have focused on this automation using traditional
machine learning-based algorithms and convolutional architectures. However, the
past works hardly focused on essential parts of the retinal image to improve
the model performance. In this study, we adopt and fine-tune transformer-based
learning models to capture the crucial features of retinal images for a more
nuanced understanding of DR severity. Additionally, we explore the
effectiveness of image transformers to infer the degree of DR severity from
fundus photographs. For experiments, we utilized the publicly available
APTOS-2019 blindness detection dataset, where the performances of the
transformer-based models were quite encouraging.
