PrecisionMicro-DETR: enhancing small pulmonary nodule detection in CT scans with multi-scale feature fusion and lightweight design

To address the common issue of insufficient accuracy in existing detection models when dealing with morphologically complex and minute pulmonary nodules, this study proposes an enhanced detection model called PrecisionMicro-DETR based on the RT-DETR architecture. The model introduces a feature enhancement fusion module tailored for small targets in the detection head to strengthen the feature extraction capability for subtle structures (Strengthen the integration of small target features, SSTF). It also incorporates a Modulation Fusion Module (MFM) to effectively improve discriminative performance in areas with blurred boundaries between lesions and normal tissues. Additionally, a lightweight neck network based on SNI-GSConvE is introduced to optimize computational load while maintaining high accuracy. Experimental evaluation shows that PrecisionMicro-DETR achieves a mean average precision (mAP) of 94.9% on the publicly available Tianchi dataset. Its robustness and generalization ability in real diagnostic environments are further validated through clinical CT images from hospital PACS systems. This study provides a high-precision and efficient solution for CT pulmonary nodule detection, contributing positively to advancing the clinical application of intelligent assisted diagnostic systems.