AMCF-CNN: Attention-Guided Multi-Scale Cross Fusion for Reducing False Positives in Lung Nodule Detection

False-positive reduction is a critical step in the automatic lung nodule detection system, playing a significant role in the early detection and diagnosis of lung cancer. However, accurately distinguishing between nodules and non-nodules remains a challenge due to their high morphological similarity. To effectively reduce the false positives (FPs) in automated nodule detection using computed tomography (CT), we propose an attention-guided multi-scale cross-fusion three-dimensional (3D) convolutional neural network (AMCF-CNN). The proposed network adopts an innovative multi-scale cross-fusion strategy to integrate nodule features across different scales and incorporates a SimAM-Res module to adaptively enhance key features, thereby improving overall feature representation. To further enhance global contextual awareness, a Global Modeling Module (GMM) based on self-attention mechanism is introduced, enabling complementary fusion of local structural details and global semantic context, thereby enhancing structural discrimination in complex imaging backgrounds. We conducted experiments on the LUNA16 dataset consisting of 888 chest CT cases, and evaluated model performance using 10-fold cross-validation. Experimental results demonstrate that the proposed model achieves sensitivities of 0.983 and 0.984 at 4 and 8 FPs/scan, respectively, with a balanced accuracy of 0.983 and a competitive performance metric (CPM) score of 0.936. Compared to existing methods, AMCF-CNN achieves competitive performance in false-positive reduction, offering a more accurate and robust solution for lung nodule detection and demonstrating strong potential for real-world clinical applications.