Lung　cancer　stands　as　the　leading　cause　of　cancer　related　mortality　worldwide.　Precise　and　automated　identification　of　lung　nodules　through　3D　Computed　Tomography　(CT)　scans　is　an　essential　part　of　screening　for　lung　cancer　effectively.　Due　to　the　small　size　of　pulmonary　nodules　and　the　close　correlation　between　neighboring　slices　of　3D　CT　images,　most　of　the　existing　methods　only　consider　the　characteristics　of　a　single　slice,　thus　easily　lead　to　insufficient　detection　accuracy　of　pulmonary　nodules.　To　solve　this　problem,　this　paper　proposes　a　Channel　Shuffle　Slice-Aware　Network　(CSSANet),　which　aims　to　fully　exploit　the　spatial　correlation　between　slices　and　effectively　utilize　the　intra-slice　features　and　inter-slice　contextual　information　to　achieve　accurate　detection　of　lung　nodules.　Specifically,　we　design　a　Group　Shuffle　Attention　module　(GSA　module)　to　fuse　the　inter-slice　feature　in　order　to　enhance　the　discrimination　and　extraction　of　corresponding　shape　information　of　distinct　nodules　in　the　same　group　of　slices.　Experiments　and　ablation　study　on　a　publicly　available　LUNA16　dataset　demonstrate　that　the　proposed　method　can　enhance　the　detection　sensitivity　effectively.　The　Competition　Performance　Metric　(CPM)　score　of　89.8　%　is　superior　over　other　representative　detection　models.　©　2024