Lung　cancer　remains　one　of　the　leading　causes　of　mortality　worldwide,　and　early　detection　is　crucial　for　improving　patient　survival　rates.　Traditional　lung　nodule　detection　methods　are　inefficient　and　inaccurate,　making　them　inadequate　for　clinical　needs.　Although　deep　learning　methods　have　made　progress　in　medical　image　analysis,　existing　approaches　still　perform　poorly　in　detecting　small,　morphologically　complex　lung　nodules,　leading　to　missed　detections　and　false　positives.　Additionally,　the　high　computational　complexity　of　previous　models　hinders　real-time　detection.　To　address　these　challenges,　this　study　proposes　a　Transformer-　based　lung　nodule　detection　model　called　LN-DETR.　The　model　integrates　a　Partial　Convolution-based　Efficient　Multi-scale　Attention　(PC-EMA)　module,　a　Grouped　and　Shuffled　Convolutional　Cross-scale　Feature　Fusion　(GS-CCFM)　module,　and　introduces　a　Channel　Transformer　(CTrans)　module.　PC-EMA　combines　Efficient　Multi-Scale　Attention　with　partial　convolution　to　enhance　multi-scale　feature　extraction　while　optimizing　computational　efficiency.　GS-CCFM　uses　Grouped　and　Shuffled　Convolution　(GSConv)　to　achieve　efficient　cross-　scale　feature　fusion.　The　CTrans　module　employs　a　cross-channel　attention　mechanism　to　further　strengthen　feature　fusion　capabilities.　Experimental　results　on　the　LUNA16　and　Tianchi　lung　nodule　datasets　demonstrate　that　LN-DETR　outperforms　existing　object　detection　models　in　detection　accuracy,　computational　efficiency,　and　model　complexity.　On　the　LUNA16　dataset,　LN-DETR　achieved　an　F1　score　of　91.5%　and　a　mean　Average　Precision　(mAP)　of　93.1%;　on　the　Tianchi　dataset,　the　F1　score　was　87.4%　and　the　mAP　was　86.4%,　both　significantly　higher　than　baseline　models.　Furthermore,　the　reduced　parameter　count　and　computational　overhead　make　the　model　more　suitable　for　broader　clinical　applications.