A　method　was　proposed　to　detect　pulmonary　nodules　in　low-dose　computed　tomography　(CT)　images　by　two-dimensional　convolutional　neural　network　under　the　condition　of　fine　image　preprocessing.　Firstly,　CT　image　preprocessing　was　carried　out　by　image　clipping,　normalization　and　other　algorithms.　Then　the　positive　samples　were　expanded　to　balance　the　number　of　positive　and　negative　samples　in　convolutional　neural　network.　Finally,　the　model　with　the　best　performance　was　obtained　by　training　two-dimensional　convolutional　neural　network　and　constantly　optimizing　network　parameters.　The　model　was　evaluated　in　Lung　Nodule　Analysis　2016(LUNA16)　dataset　by　means　of　five-fold　cross　validation,　and　each　group＇s　average　model　experiment　results　were　obtained　with　the　final　accuracy　of　92.3%,　sensitivity　of　92.1%　and　specificity　of　92.6%.Compared　with　other　existing　automatic　detection　and　classification　methods　for　pulmonary　nodules,　all　indexes　were　improved.　Subsequently,　the　model　perturbation　experiment　was　carried　out　on　this　basis.　The　experimental　results　showed　that　the　model　is　stable　and　has　certain　anti-interference　ability,　which　could　effectively　identify　pulmonary　nodules　and　provide　auxiliary　diagnostic　advice　for　early　screening　of　lung　cancer.　Copyright　©　2019　by　Editorial　Office　of　Journal　of　Biomedical　Engineering.