METHODS article
Front. Comput. Sci.
Sec. Computer Vision
This article is part of the Research TopicLarge Tensor Analysis and ApplicationsView all 6 articles
PrecisionMicro-DETR: Enhancing Small Pulmonary Nodule Detection in CT Scans with Multi-Scale Feature Fusion and Lightweight Design
Provisionally accepted
- 1Guangzhou University of Traditional Chinese Medicine ShunDe Traditional Chinese Medicine Hospital, Foshan, China
- 2Macau University of Science and Technology, Taipa, Macao, SAR China
- 3Wuyi University, Jiangmen, China
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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.
Keywords: CT images, Multi-scale features, object detection, Pulmonary nodule detection, RT-DETR
Received: 09 Dec 2025; Accepted: 27 Jan 2026.
Copyright: © 2026 Li, Chen, Zhu, Lin, Deng, Fu and Liao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Huilian Liao
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