TCMNet: dynamic tanh enhanced 3D transformer with cascaded multiplicative fusion for pulmonary nodule detection

Early detection of pulmonary nodules plays a critical role in improving the survival rate of patients with lung cancer. However, existing detection methods often exhibit limited feature stability and inadequate contextual awareness, particularly when handling small nodules or nodules with complex structures. To address these limitations, we propose a 3D detection method, termed TCMNet. The proposed model consists of two key components. The SD module incorporates a dynamic tanh nonlinear transformation into the Swin Transformer to improve semantic retention and feature stability in regions containing small targets. The CMCA module combines cascaded multiplicative connections with a Monte Carlo based attention mechanism to enhance multi-scale feature interaction and increase the focus of the model on pulmonary nodules. Experiments on the LUNA16 dataset show that TCMNet achieves an average FROC score of 91.59%, outperforming several state-of-the-art methods. These results indicate that the proposed architecture provides a robust and effective solution for three-dimensional pulmonary nodule detection.