{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:07:08Z","timestamp":1790788028976,"version":"4.1.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Lung cancer detection using computed tomography (CT) imaging is a critical task for early diagnosis and improved patient outcomes. However, accurate identification of small and low-contrast pulmonary nodules remains challenging due to variations in nodule size, shape, and complex background interference. To overcome these challenges, we propose HARM-YOLO, an enhanced object detection framework based on YOLOv10, specifically designed for lung cancer detection in CT scans. Our model incorporates a multi-dimensional receptive field feature extractor (C2f-MDR), a decoupled neck architecture (DENeck), series and parallel receptive field enhancement modules (SRFEM and PRFEM), and a background attention mechanism to strengthen multi-scale feature representation and suppress irrelevant signals. Extensive experiments on the LIDC-IDRI and LUNA16 datasets demonstrate that HARM-YOLO achieves a mean average precision (mAP@0.5) of 91.3% and sensitivity of 92.7%, outperforming state-of-the-art methods including YOLOv5, ELCT-YOLO, and MSG-YOLO by significant margins. With an optimal balance of 92.7% sensitivity and 89.7% precision, our framework effectively detects true nodules while minimizing false positives, addressing key needs for computer-aided diagnosis in clinical screening. Furthermore, compared against segmentation-based approaches such as nnUNet and Swin-UNet, HARM-YOLO maintains superior performance on small nodules (\u22646 mm) and real-time inference speed suitable for large-scale lung cancer screening programs. Our results highlight the potential of this YOLOv10-based object detection system as a robust and efficient tool for enhancing early lung cancer detection and supporting clinical decision-making.<\/jats:p>","DOI":"10.3389\/fonc.2025.1698698","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T05:24:30Z","timestamp":1767331470000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["An enhanced YOLOv10 architecture for high-sensitivity and high-specificity lung cancer 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