Accurate　detection　of　pulmonary　nodules　on　chest　computed　tomography　scans　is　crucial　to　early　diagnosis　of　lung　cancer.　To　address　the　thorn　problems　on　low　detection　sensitivity　and　high　false-positive　rate　caused　by　heterogeneity　and　morphological　complexity　of　3-D　nodule　features,　a　computer-aided　detection　system　is　developed　to　increase　the　detection　sensitivity　and　classification　accuracy　of　pulmonary　nodules.　The　contributions　include:　(1)　Nodule　candidate　detection:　3-D　Residual　U-Net　model　is　improved　to　detect　candidate　nodules,　which　constructs　3-D　context-guided　module　to　extract　local　and　global　nodule　features　by　setting　the　dilated　convolution　with　different　dilation　rates.　Furthermore,　channel　attention　mechanism　is　used　to　dynamically　adjust　the　channel　features,　which　enhances　the　generalization　and　expression　ability　of　the　detection-network　to　effectively　learn　3-D　spatial　context　features.　(2)　False-positive　reduction:　multi-branch　classification　network　is　designed　for　multi-task　learning.　Image　reconstruction　task　is　performed　to　retain　more　microscopic　nodules　information　from　convolutional　neural　network　(CNN)　hierarchy.　Moreover,　each　branch　deals　with　the　feature　map　at　corresponding　depth　layers,　and　then　all　branches＇　feature　maps　are　combined　together　to　perform　nodule　classification　task.　Numerous　experimental　results　show　that　the　proposed　system　is　perfectly　qualified　for　pulmonary　nodules　detection　on　Lung　Nodules　Analysis　2016　dataset,　which　achieves　detection　sensitivity　up　to　94.0%　and　competition　performance　metric　(CPM)　score　up　to　0.959.　©　2013　IEEE.