Cancer　is　one　of　the　diseases　with　high　mortality　rates　in　the　21st　century,　with　lung　cancer　being　the　first　in　all　cancer　morbidity　and　mortality　rates.　In　recent　years,　with　the　large　rise　of　data　and　artificial　intelligence　researches,　the　auxiliary　diagnosis　of　lung　cancer　based　on　deep　learning　has　gradually　become　a　hot　research　topic.　As　the　available　and　public　datasets　for　lung　cancer　are　mainly　CT　scans　images　with　lung　nodules　annotations,　the　work　on　the　assisted　diagnosis　of　lung　cancer　using　deep　learning　is　mainly　based　on　image　data　preprocessing,　Pulmonary　nodule　segmentation,　and　lesion　analysis　and　diagnosis.　Computer-aided　diagnosis　(CAD)　tools　help　radiologists　to　reduce　diagnostic　errors　such　as　missing　tumors　and　misdiagnosis.　So　our　aim　is　to　help　the　development　of　a　new　CAD　system　with　higher　performance　than　the　existent　ones　to　assist　lung　cancer　detection　in　early　stages.　This　paper　presents　an　overview　of　the　deep　learning　methods　used　for　computer-aided　lung　cancer　detection　and　diagnosis.　It　is　mainly　focused　on　the　important　processing　and　analyzing　methods　for　the　pulmonary　image　data　obtained　by　medical　instrument　imaging,　and　which　we　can　summarize　into　these　4　steps:　Medical　image　data　preprocessing,　Pulmonary　nodule　segmentation,　pulmonary　nodule　detection,　and　finally　lesion　diagnosis　A　full　description　of　the　CAD　systems　steps　is　given　along　with　an　overview　of　the　state　of　art　deep　learning　medical　image　processing　methods.　©　2019　ACM.