An enhanced YOLOv10 architecture for high-sensitivity and high-specificity lung cancer detection

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 (≤6 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.