Lung　cancer　is　the　main　malignant　tumour　affecting　the　health　of　residents　in　China.　Automatically　discriminating　benign　and　malignant　pulmonary　nodules　can　facilitate　the　early　detection　of　lung　cancer,　which　reduces　lung　cancer　mortality.　The　rising　quantity　of　public　available　lung　CT　datasets　made　it　possible　to　use　deep　learning　approaches　for　lung　nodules　malignancy　classification.　Unlike　most　of　the　previous　models　that　focused　on　2D　convolutional　neural　nets　(CNN),　here　we　explore　the　use　of　the　DenseNet　architecture　with　3D　filters　and　pooling　kernels.　The　performance　of　the　proposed　nodule　classification　was　evaluated　on　publicly　available　LUNA16　dataset,　a　subset　of　lung　image　database　consortium　and　image　database　resource　initiative　dataset　(LIDC/IDRI).　It　achieved　a　92.4%　classification　accuracy.　The　proposed　method　provides　an　independent　module　with　encouraging　prediction　accuracy　that　can　be　easily　incorporated　with　a　lung　cancer　computer-aided　diagnosis　system.　©　Published　under　licence　by　IOP　Publishing　Ltd.