A retinopathy detection method based on improved YOLO

To improve the efficiency of diagnosing retinal diseases, deep learning-based object detection methods are employed for auxiliary diagnosis. Given the excellent performance of the You Only Look Once (YOLO) series models in the field of object detection, this paper proposes an improved YOLO model based on the YOLOv8n for detecting fundus images. Firstly, to enhance the model's ability to detect small objects, the Concat in the neck network of YOLOv8n is replaced with BiFPN. Additionally, considering that lesions in fundus images often have irregular and blurred boundaries, the original CIoU loss function in YOLOv8n is replaced with the Inner-IoU loss function to improve the model's recognition accuracy. Experimental results show that the Mean Average Precision at IoU=0.5 ([email protected]) of the improved YOLO model reaches 0.963, which is an improvement of 2.2% and 13.8% compared to the baseline model YOLOv8n and the classic object detection model Faster-RCNN, respectively