GDG-YOLO: A dual-branch lightweight network based on global context augmentation for laryngeal disease detection

Early and accurate detection of laryngeal lesions is essential for clinical diagnosis and treatment. However, conventional laryngoscopic assessment is subjective and prone to oversight. Although deep learning-based methods have shown promise, they often suffer from poor sensitivity to small lesions, vulnerability to illumination variations and specular reflections, and dependence on limited annotated data. This study aims to develop a lightweight detection model that balances detection performance and computational complexity for laryngeal lesion detection.

We developed GDG-YOLO, a task-tailored lightweight detection framework designed to address three prevalent challenges in laryngoscopic imaging: limited feature representation of small lesions, feature instability under heterogeneous illumination, and localization uncertainty associated with blurred or irregular lesion boundaries. This framework adaptively adjusts and integrates established architectural components. First, a mature Global Context Block is embedded into the C3k2 structure to enhance contextual feature representation. Second, a task-specific dual-path neck is designed to combine deep semantic information with high-resolution spatial features for multi-scale feature fusion. Third, a lightweight detection head incorporates grouped convolutions and distribution focal loss-based regression to strike a balance between localization performance and computational complexity. Furthermore, AugMix data augmentation is applied to minority classes in the training set to increase sample diversity and alleviate class imbalance. Finally, the proposed framework is evaluated on a single-center clinical laryngoscopic image dataset.

GDG-YOLO achieved an mAP50 of 87.6%, representing a 5.6-percentage-point improvement over the baseline YOLOv11n. Ablation studies and statistical analyses demonstrated that the complete GDG-YOLO architecture significantly outperformed the YOLOv11n baseline across all evaluated metrics. In addition, comparative experiments showed that GDG-YOLO achieved higher Precision, F1-score, mAP50, and mAP50:95 than the evaluated representative detection models, including YOLOv10n, RT-DETR, and SSD, under the current experimental setting.