An Improved Microaneurysm Detection Model Based on SwinIR and YOLOv8

• Published in: Bioengineering (Basel) 2023-12, Vol.10 (12), p.1405
• Main Authors: Zhang, Bowei, Li, Jing, Bai, Yun, Jiang, Qing, Yan, Biao, Wang, Zhenhua
• Format: Article
• Language: English
• Subjects: Aneurysms; Angiography; Bioengineering; Blood vessels; Datasets; Deep learning; Design; Diabetes mellitus; Diabetic retinopathy; Diagnosis; Efficiency; Health aspects; Image reconstruction; Image resolution; Localization; Machine learning; Machine vision; Medical imaging; microaneurysm; Microvasculature; Neural networks; Retinopathy; Semantics; SwinIR; Transfer learning; YOLOv8
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10121405

Diabetic retinopathy (DR) is a microvascular complication of diabetes. Microaneurysms (MAs) are often observed in the retinal vessels of diabetic patients and represent one of the earliest signs of DR. Accurate and efficient detection of MAs is crucial for the diagnosis of DR. In this study, an automatic model (MA-YOLO) is proposed for MA detection in fluorescein angiography (FFA) images. To obtain detailed features and improve the discriminability of MAs in FFA images, SwinIR was utilized to reconstruct super-resolution images. To solve the problems of missed detection of small features and feature information loss, an MA detection layer was added between the neck and the head sections of YOLOv8. To enhance the generalization ability of the MA-YOLO model, transfer learning was conducted between high-resolution images and low-resolution images. To avoid excessive penalization due to geometric factors and address sample distribution imbalance, the loss function was optimized by taking the Wise-IoU loss as a bounding box regression loss. The performance of the MA-YOLO model in MA detection was compared with that of other state-of-the-art models, including SSD, RetinaNet, YOLOv5, YOLOX, and YOLOv7. The results showed that the MA-YOLO model had the best performance in MA detection, as shown by its optimal metrics, including recall, precision, F1 score, and AP, which were 88.23%, 97.98%, 92.85%, and 94.62%, respectively. Collectively, the proposed MA-YOLO model is suitable for the automatic detection of MAs in FFA images, which can assist ophthalmologists in the diagnosis of the progression of DR.