Pulmonary　nodule　detection　with　low-dose　computed　tomography　(LDCT)　is　indispensable　in　early　lung　cancer　screening.　Although　existing　methods　have　achieved　excellent　detection　sensitivity,　nodule　detection　still　faces　challenges　such　as　nodule　size　variation　and　uneven　distribution,　as　well　as　excessive　nodule-like　false　positive　candidates　in　the　detection　results.　We　propose　a　novel　two-stage　nodule　detection　(TSND)　method.　In　the　first　stage,　a　multi-scale　feature　detection　network　(MSFD-Net)　is　designed　to　generate　nodule　candidates.　This　includes　a　proposed　feature　extraction　network　to　learn　the　multi-scale　feature　representation　of　candidates.　In　the　second　stage,　a　candidate　scoring　network　(CS-Net)　is　built　to　estimate　the　score　of　candidate　patches　to　realize　false　positive　reduction　(FPR).　Finally,　we　develop　an　end-to-end　nodule　computer-aided　detection　(CAD)　system　based　on　the　proposed　TSND　for　LDCT　scans.　Experimental　results　on　the　LUNA16　dataset　show　that　our　proposed　TSND　obtained　an　excellent　average　sensitivity　of　90.59%　at　seven　predefined　false　positives　(FPs)　points:　0.125,　0.25,　0.5,　1,　2,　4,　and　8　FPs　per　scan　on　the　FROC　curve　introduced　in　LUNA16.　Moreover,　comparative　experiments　indicate　that　our　CS-Net　can　effectively　suppress　false　positives　and　improve　the　detection　performance　of　TSND.