Research on lung nodule detection in X-ray plain films based on improved YOLOv12 algorithm

To investigate the feasibility of automatic lung nodule detection using chest X-rays, this study proposes an improved YOLOv12 algorithm based on space-to-depth convolution (SPDConv), a dynamic upsampling module (DySample), and a one-shot aggregation cross stage partial network with ghost convolution (VoVGSCSP). The original YOLOv12 algorithm was optimized by replacing specific convolutional layers in the Backbone and Neck with SPDConv, substituting the Upsample modules in the Neck with upgraded DySample modules, and replacing the C3k2 and A2C2f modules in the Neck with VoVGSCSP to construct the YOLOv12-SPDConv-Dysample-VoVGSCSP algorithm. The optimized algorithm was trained and validated using a public chest X-ray lung nodule dataset available on the Roboflow platform, and its performance was compared with that of the original YOLOv12 algorithm. Results indicate that the improved algorithm achieved a mean average precision at an intersection over union threshold of 0.5 (mAP50) of 0.735 and a mAP50-95 of 0.426 in detecting lung nodules on chest X-rays. These results outperformed the original YOLOv12 algorithm, which achieved a mAP50 of 0.704 and a mAP50-95 of 0.411. In conclusion, the YOLOv12-SPDConv-Dysample-VoVGSCSP algorithm demonstrates superior overall performance in detecting lung nodules on chest X-rays, significantly surpassing the original YOLOv12 algorithm.