Optimization of lung nodule detection model based on YOLOv8

Lung cancer is one of the malignancies with the highest incidence and mortality rates worldwide. Accurate detection of early lung nodules is crucial for improving patient survival. However, manual reading of medical images carries risks of missed or incorrect diagnoses, highlighting the urgent need for efficient and precise automated detection methods. This study constructs and optimizes a lung nodule detection model based on YOLOv8. Model adjustments include tuning learning rate strategies, weight decay, and color augmentation parameters. Experiments were conducted on the standard LIDC-IDRI dataset. Model performance was evaluated using recall, Mean Average Precision (mAP), and the confusion matrix. The results indicate a recall rate of 0.87 and an mAP of 0.776, with a correct lung nodule recognition rate of 0.76 in the confusion matrix. The study demonstrates that rational parameter optimization can effectively reduce missed detections and misjudgments while significantly improving detection accuracy and classification stability. This provides a feasible technical pathway for computer-aided early lung cancer diagnosis. Future work may further enhance the models application value in intelligent medical imaging analysis by integrating data augmentation with parameter optimization.