While　deep　learning　techniques,　such　as　Convolutional　neural　networks　(CNNs),　show　significant　potential　in　medical　applications,　real-time　detection　of　parathyroid　glands　(PGs)　during　complex　surgeries　remains　insufficiently　explored,　posing　challenges　for　surgical　accuracy　and　outcomes.　Previous　studies　highlight　the　importance　of　leveraging　prior　knowledge,　such　as　shape,　for　feature　extraction　in　detection　tasks.　However,　they　fail　to　address　the　critical　multi-scale　variability　of　PG　objects,　resulting　in　suboptimal　performance　and　efficiency.　In　this　paper,　we　propose　an　end-to-end　framework,　MSWF-PGD,　for　Multi-Scale　Weighted　Fusion　Parathyroid　Gland　Detection.　To　improve　accuracy　and　efficiency,　our　approach　extracts　feature　maps　from　convolutional　layers　at　multiple　scales　and　re-weights　them　using　cluster-aware　multi-scale　alignment,　considering　diverse　attributes　such　as　the　size,　color,　and　position　of　PGs.　Additionally,　we　introduce　Multi-Scale　Aggregation　to　enhance　scale　interactions　and　enable　adaptive　multi-scale　feature　fusion,　providing　precise　and　informative　locality　information　for　detection.　Extensive　comparative　experiments　and　ablation　studies　on　the　parathyroid　dataset　(PGsdata)　demonstrate　the　proposed　framework＇s　superiority　in　accuracy　and　real-time　efficiency,　outperforming　state-of-the-art　models　such　as　RetinaNet,　FCOS,　and　YOLOv8.