Research on Improving the High Precision and Lightweight Diabetic Retinopathy Detection of YOLOv8n

TL;DR

This paper introduces YOLO-KFG, a lightweight and high-precision model for diabetic retinopathy detection that improves micro-lesion perception and reduces parameters, outperforming existing algorithms in accuracy and efficiency.

Contribution

The paper proposes novel modules and an architecture enhancement to YOLOv8n, significantly improving detection accuracy and model efficiency for diabetic retinopathy.

Findings

Parameter count reduced by 20.7%

mAP@0.5 increased by 4.1%

Recall rate improved by 7.9%

Abstract

Early detection and diagnosis of diabetic retinopathy is one of the current research focuses in ophthalmology. However, due to the subtle features of micro-lesions and their susceptibility to background interference, ex-isting detection methods still face many challenges in terms of accuracy and robustness. To address these issues, a lightweight and high-precision detection model based on the improved YOLOv8n, named YOLO-KFG, is proposed. Firstly, a new dynamic convolution KWConv and C2f-KW module are designed to improve the backbone network, enhancing the model's ability to perceive micro-lesions. Secondly, a fea-ture-focused diffusion pyramid network FDPN is designed to fully integrate multi-scale context information, further improving the model's ability to perceive micro-lesions. Finally, a lightweight shared detection head GSDHead is designed to reduce the model's parameter count, making it more deployable on re-source-constrained devices. Experimental results show that compared with the base model YOLOv8n, the improved model reduces the parameter count by 20.7%, increases mAP@0.5 by 4.1%, and improves the recall rate by 7.9%. Compared with single-stage mainstream algorithms such as YOLOv5n and YOLOv10n, YOLO-KFG demonstrates significant advantages in both detection accuracy and efficiency.