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Early detection of lung nodules is crucial for the prevention and treatment of lung cancer. However, current methods face challenges such as missing small nodules, variations in nodule size, and high false positive rates. To address these challenges, we propose a Global Channel Spatial Attention Mechanism (GCSAM). Building upon it, we develop a Candidate Nodule Detection Network (CNDNet) and a False Positive Reduction Network (FPRNet). CNDNet employs Res2Net as its backbone network to capture multi-scale features of lung nodules, utilizing GCSAM to fuse global contextual information, adaptively adjust feature weights, and refine processing along the spatial dimension. Additionally, we design a Hierarchical Progressive Feature Fusion (HPFF) module to effectively combine deep semantic information with shallow positional information, enabling high-sensitivity detection of nodules of varying sizes. FPRNet significantly reduces the false positive rate by accurately distinguishing true nodules from similar structures. Experimental results on the LUNA16 dataset demonstrate that our method achieves a competitive performance metric (CPM) value of 0.929 and a sensitivity of 0.977 under 2 false positives per scan. Compared to existing methods, our proposed method effectively reduces false positives while maintaining high sensitivity, achieving competitive results.
Li et al. (Thu,) studied this question.