Utilizing　machine　learning　methods　for　auxiliary　decision　support　in　medical　imaging　significantly　reduces　missed　detections　and　unnecessary　expenses.　However,　the　strict　accuracy　and　transparency　requirements　in　the　medical　field　pose　challenges　for　deep　learning　applications　based　on　neural　networks.　To　address　these　issues,　we　propose　a　novel　artificial　intelligence　artifact　guided　by　the　design　science　research　methodology　for　lesion　detection　decision　support　in　medical　images,　called　Explainable　Lesion　DEtection　TRansformer　(EL-DETR).　This　approach　features　an　explainable　separate　attention　mechanism　in　the　decoder　that　highlights　the　attention　weights　of　content　and　location　queries,　providing　insights　into　the　inference　process　through　attention　mapping　visualizations.　In　addition,　we　introduce　a　hybrid　matching　query　strategy　to　enhance　the　learning　of　positive　samples　and　develop　an　adaptive　efficient　compound　loss　function　to　optimize　training.　We　demonstrate　EL-DETR＇s　superior　accuracy,　robustness,　and　interpretability　using　four　real-world　datasets,　establishing　it　as　a　reliable　tool　for　clinical　diagnosis　and　treatment　decision　support　based　on　medical　imaging.　The　code　and　original　data　are　available　at　https://github.com/weimingai/EL-DETR.　©　2025