Automatic　and　accurate　detection　of　chest　X-ray　lesion　is　a　challenging　task.　In　the　chest　X-ray　image,　the　lesions　are　shown　with　blurred　boundary　contours,　different　sizes,　variable　shapes,　uneven　density,　etc.　Besides,　the　deep　convolutional　neural　network　(CNN)　consists　of　traditional　convolution　units,　which　has　the　limitations　of　rectangular　sampling.　The　CNN　extracts　difficultly　the　deformation　and　refined　features　of　chest　X-ray　lesions.　Because　of　these　factors,　the　accuracy　of　the　lesion　detection　algorithm　is　not　high.　To　deal　with　problems,　we　propose　the　deformation　and　refined　features　based　lesion　detection　on　the　chest　X-ray　algorithm　called　DRCXNet.　Firstly,　the　deformable　convolution　with　amplitude　modulation　(AMDCN)　is　built　to　extract　the　deformation　features　of　the　lesions　on　the　chest　X-ray.　Secondly,　to　obtain　the　refined　feature,　the　global　features　and　local　features　are　fused,　which　can　enrich　the　feature　space　of　the　lesion.　Thirdly,　the　pooling　layer　combines　with　the　AMDCN　and　region　proposal　network　to　establish　the　deformable　pooling　layer,　which　enhances　the　model＇s　sensitivity　to　the　lesion　location.　During　the　training,　the　model　is　optimized　by　the　improved　regression　loss　function　with　a　gradient　control　factor.　On　the　public　datasets　RSNA　and　ChestX-ray8,　the　proposed　method　outperforms　seven　popular　detection　algorithms.　The　proposed　method　is　a　significant　performance　in　both　qualitative　and　quantitative　experiments.　Its　comprehensive　evaluation　scores,　sensitivity,　precision,　and　the　mean　dice　similarity　coefficient　are　0.866,　0.914,　0.836　and　0.859　respectively.　The　proposed　algorithm　achieves　a　very　satisfactory　result.　©　2013　IEEE.