FFD-YOLO11: A Lightweight and High-Precision Framework for Automated Fundus Disease Detection

Automated detection of ocular lesions from fundus images is of great significance for disease screening and early diagnosis. However, existing methods are often constrained by the trade-off between model accuracy and computational efficiency, particularly under resource-limited hardware conditions, where missed detections and false positives are common.In this paper, we propose FFD-YOLO11 (Fundus Disease Detection-YOLO11), a novel and efficient detection framework based on the YOLO11 architecture. The proposed model integrates the RepViT structure and Efficient Multi-scale Attention (EMA) into the backbone to enhance the representation of pathological features. Moreover, a Large Separable Kernel Attention (LSKA) mechanism is embedded in the Spatial Pyramid Pooling Fast (SPPF) module to expand the receptive field and strengthen contextual feature modeling.Furthermore, we design a Lightweight Shared Convolutional Detection Head (LSCD) and a Feature Diffusion Pyramid Network (FDPN), which effectively fuse multi-scale features while significantly reducing the model parameters. Experimental results show that FFD-YOLO11 achieves 97.4% mAP with only 2.6M parameters, outperforming the baseline by 5.1% and achieving the best performance among comparable models. Visualization analysis further demonstrates the model’s precise focus and localization of clinically critical lesion regions.Overall, FFD-YOLO11 provides a high-accuracy, lightweight, and robust detection solution suitable for clinical environments and embedded medical imaging systems, offering a new technological approach for intelligent ophthalmic diagnosis assistance.