Purpose　Considering　that　false-positive　and　true　pulmonary　nodules　are　highly　similar　in　shapes　and　sizes　between　lung　computed　tomography　scans,　we　develop　and　evaluate　a　false-positive　nodules　reduction　method　applied　to　the　computer-aided　diagnosis　system.　Methods　To　improve　the　pulmonary　nodule　diagnosis　quality,　a　3D　convolutional　neural　networks　(CNN)　model　is　constructed　to　effectively　extract　spatial　information　of　candidate　nodule　features　through　the　hierarchical　architecture.　Furthermore,　three　paths　corresponding　to　three　receptive　field　sizes　are　adopted　and　concatenated　in　the　network　model,　so　that　the　feature　information　is　fully　extracted　and　fused　to　actively　adapting　to　the　changes　in　shapes,　sizes,　and　contextual　information　between　pulmonary　nodules.　In　this　way,　the　false-positive　reduction　is　well　implemented　in　pulmonary　nodule　detection.　Results　Multi-path　3D　CNN　is　performed　on　LUNA16　dataset,　which　achieves　an　average　competitive　performance　metric　score　of　0.881,　and　excellent　sensitivity　of　0.952　and　0.962　occurs　to　4,　8　FP/Scans.　Conclusion　By　constructing　a　multi-path　3D　CNN　to　fully　extract　candidate　target　features,　it　accurately　identifies　pulmonary　nodules　with　different　sizes,　shapes,　and　background　information.　In　addition,　the　proposed　general　framework　is　also　suitable　for　similar　3D　medical　image　classification　tasks.