A Pulmonary ground-glass nodule density recognition model based on the GGNs-YOLOv11 neural network

Pulmonary ground-glass nodules (GGNs) are important imaging biomarkers for early detection and diagnosis of lung tumors. Accurate identification and classification of GGN density remain challenging because of the subtle radiological features and variability in the clinical presentation of GGNs. This study aimed to develop a new recognition model based on the GGNs-YOLO (You Only Look Once) v11 neural network to improve detection accuracy and robustness in medical imaging. The model enhanced YOLOv11s by integrating a Mixed Aggregation Network into its backbone to replace the traditional C3k2 module. This integration substantially improved the model's ability and accuracy to detect GGNs. Additionally, we proposed using the efficient up-convolution block to replace traditional upsampling and introducing the Inner-Complete Intersection over Union loss function to boost bounding box regression precision. These modifications improved the model's performance in detecting small lung nodules. Finally, we compared the GGNs-YOLOv11 model with other existing models to validate our proposed enhancements. The results showed that the improved GGNs-YOLOv11 model outperformed other models in GGN detection tasks. Specifically, its mAP50 on the dataset reached 93.6%, with a recall of 88.6% and accuracy of 89.9%, which were 4.7%, 5.1%, and 3% higher than those of the baseline model, respectively. This approach not only improved diagnostic reliability but also provided a practical tool to assist radiologists in clinical decision-making. This study highlights the potential of deep learning-based detection frameworks to advance intelligent medical imaging and support early lung cancer screening.