Detecting Severity of Diabetic Retinopathy from Fundus Images: A Transformer Network-based Review
This addresses the need for automated diagnosis to assist ophthalmologists in detecting vision-threatening conditions in diabetic patients, but it is incremental as it applies existing transformer methods to a known medical imaging task.
The paper tackled automated severity staging of diabetic retinopathy from fundus images by fine-tuning transformer-based models, achieving encouraging performance on the APTOS-2019 dataset.
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.