Nonnegative　sparse　representation　has　become　a　popular　methodology　in　medical　analysis　and　diagnosis　in　recent　years.　In　order　to　resolve　network　degradation,　higher　dimensionality　in　feature　extraction,　data　redundancy,　and　other　issues　faced　when　medical　images　parameters　are　trained　using　convolutional　neural　networks.　Lung　tumors　in　chest　CT　image　based　on　nonnegative,　sparse,　and　collaborative　representation　classification　of　DenseNet　(DenseNet-NSCR)　are　proposed　by　this　paper:　firstly,　initialization　parameters　of　pretrained　DenseNet　model　using　transfer　learning;　secondly,　training　DenseNet　using　CT　images　to　extract　feature　vectors　for　the　full　connectivity　layer;　thirdly,　a　nonnegative,　sparse,　and　collaborative　representation　(NSCR)　is　used　to　represent　the　feature　vector　and　solve　the　coding　coefficient　matrix;　fourthly,　the　residual　similarity　is　used　for　classification.　The　experimental　results　show　that　the　DenseNet-NSCR　classification　is　better　than　the　other　models,　and　the　various　evaluation　indexes　such　as　specificity　and　sensitivity　are　also　high,　and　the　method　has　better　robustness　and　generalization　ability　through　comparison　experiment　using　AlexNet,　GoogleNet,　and　DenseNet-201　models.