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An Improved Microaneurysm Detection Model Based on SwinIR and YOLOv8
Bowei Zhang1, Jing Li2, Yun Bai1
1College of Information Science, Shanghai Ocean University, Shanghai 201306, China.
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.
Insights
A new MA-YOLO model accurately detects microaneurysms (MAs) in diabetic retinopathy (DR) fluorescein angiography (FFA) images. This automated approach improves diagnostic accuracy for DR progression.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Diabetic retinopathy (DR) is a diabetes complication characterized by microvascular changes.
- Microaneurysms (MAs) are early indicators of DR, necessitating precise detection in retinal images.
- Accurate MA detection is vital for timely DR diagnosis and management.
Purpose of the Study:
- To develop an automated model, MA-YOLO, for detecting microaneurysms (MAs) in fluorescein angiography (FFA) images.
- To enhance MA detection accuracy by integrating super-resolution and specialized detection layers.
- To validate the MA-YOLO model's performance against existing state-of-the-art methods.
Main Methods:
- Utilized SwinIR for super-resolution reconstruction of FFA images to enhance MA feature details.
- Integrated a novel MA detection layer into the YOLOv8 architecture to address small feature detection and information loss.
- Employed transfer learning and optimized the loss function with Wise-IoU to improve model generalization and robustness.
Main Results:
- The MA-YOLO model achieved superior performance in MA detection compared to SSD, RetinaNet, YOLOv5, YOLOX, and YOLOv7.
- Achieved optimal performance metrics: 88.23% recall, 97.98% precision, 92.85% F1 score, and 94.62% AP.
- Demonstrated effectiveness in handling geometric factors and sample imbalance through loss function optimization.
Conclusions:
- The proposed MA-YOLO model is highly effective for automated microaneurysm detection in FFA images.
- This AI-driven tool can assist ophthalmologists in diagnosing and monitoring the progression of diabetic retinopathy.
- The study highlights the potential of integrating advanced AI techniques for improved ophthalmic diagnostics.
