MSF-YOLO: An Improved YOLOv10 Network for Object Detection on Lung Nodule

Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. As lung nodules are only more widely detectable once they have moved to other lung sections, it is highly challenging to anticipate the incidence of lung cancer at the beginning stages. In order to recognize lung nodules in a better and more timely manner and to prevent delays in the diagnosis of lung nodules, this paper proposes a new deep learning-based lung nodule detection model (MSF-YOLO). The model first integrates fusion of global and local features module (FGLM) into the backbone of YOLOv10n to improve the feature extraction capability of the model. Then, a new lateral cross-scale feature fusion structure (TiFPN) is added to the original feature pyramid to realize the learning of target features at different scales. Finally, the Inner Wise-MPDIoU loss function is proposed to improve the detection accuracy. The experimental results show that the proposed MSF-YOLO achieves [email protected] of 94.8% and [email protected]:0.95 of 62.9% (compared with YOLOv10-n increases by 4.6% and 4.5%, respectively)on the LIDC-IDRI dataset.