Detectron2 for Lesion Detection in Diabetic Retinopathy

Abstract

Hemorrhages in the retinal fundus are a common symptom of both diabetic retinopathy and diabetic macular edema, making their detection crucial for early diagnosis and treatment. For this task, the aim is to evaluate the performance of two pre-trained and additionally fine-tuned models from the Detectron2 model zoo, Faster R-CNN (R50-FPN) and Mask R-CNN (R50-FPN). Experiments show that the Mask R-CNN (R50-FPN) model provides highly accurate segmentation masks for each detected hemorrhage, with an accuracy of 99.34%. The Faster R-CNN (R50-FPN) model detects hemorrhages with an accuracy of 99.22%. The results of both models are compared using a publicly available image database with ground truth marked by experts. Overall, this study demonstrates that current models are valuable tools for early diagnosis and treatment of diabetic retinopathy and diabetic macular edema.