Dual-stage pulmonary nodule detection in CT scans via cross-layer attention and adaptive multi-scale 3D CNN.

Background: Early diagnosis of pulmonary nodules is crucial for improving the survival rate of lung cancer patients. However, significant variability in nodule size, shape, and anatomical location presents ongoing challenges for automated detection systems, often resulting in high false-positive rates. Objective: This study aims to develop a dual-stage pulmonary nodule detection framework based on cross-layer attention fusion, with the goal of improving sensitivity while reducing false positives in chest CT scans. Methods: We propose a two-stage detection pipeline. In the candidate detection stage, we design an Attention-guided Spatial and Channel Residual Module that integrates multi-scale residual connections with cross-dimensional attention to enhance discriminative features while preserving spatial detail. For false positive reduction, we introduce a Multi-scale Progressive Perception Network, which processes candidates across three anatomical resolutions through parallel branches and integrates top-down semantic fusion with localized attention. The model is evaluated on the LUNA16 dataset. Results: Experimental results demonstrate that the proposed method achieves a sensitivity of 90.0% at 0.55 false positives per scan on the LUNA16 dataset. Compared to state-of-the-art approaches, our framework provides a favorable balance between sensitivity and precision. Conclusions: The proposed dual-stage detection framework effectively enhances the performance of pulmonary nodule detection by incorporating cross-layer attention mechanisms and multi-scale feature integration. These findings suggest its potential for clinical deployment in computer-aided lung cancer screening.