As　a　crucial　component　of　a　computer-aided　diagnosis　(CAD)　system,　the　false-positive　reduction　plays　an　important　role　in　the　timely　diagnosis　of　pulmonary　nodules.　Own　to　the　similarity　of　the　true　and　false-positive　nodules　in　early　morphology,　it＇s　a　huge　challenge　to　distinguish　exactly　between　these　two　nodules.　Hence,　a　novel　convolutional　neural　network　(CNN)　framework　based　on　the　residual　network　is　constructed　to　address　this　thorny　issue.　The　deformable　convolution　component　is　performed　on　Computed　Tomography　(CT)　images　to　adaptively　reflect　different　spatial　information,　and　the　deformable　feature　images　can　reflect　the　complex　structure　appropriately.　This　efficient　Deformable　Convolutional　Neural　Networks　(DCNN)　model　has　been　performed　on　the　Lung　Nodule　Analysis　2016　dataset,　which　achieves　an　average　competitive　performance　metric　score　of　0.835,　and　the　excellent　sensitivity　of　0.941　and　0.958　occur　to　4,　8　false-positive　per　scan.　©　2021　IEEE.