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An Improved Microaneurysm Detection Model Based on SwinIR and YOLOv8

Authors :
Bowei Zhang
Jing Li
Yun Bai
Qing Jiang
Biao Yan
Zhenhua Wang
Source :
Bioengineering, Vol 10, Iss 12, p 1405 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
23065354
Volume :
10
Issue :
12
Database :
Directory of Open Access Journals
Journal :
Bioengineering
Publication Type :
Academic Journal
Accession number :
edsdoj.8233ddabc87b480db0761c4494199836
Document Type :
article
Full Text :
https://doi.org/10.3390/bioengineering10121405