YOLO-MSRF for lung nodule detection

(Aim) Aiming at the problem that there are a large number of small object nodules that are difficult to detect in lung images, detection methods based on improved YOLOv7 are proposed in this paper. (Method) First, a new small object detection layer (SODL) is proposed to solve the problem of the small size and irregular shape of lung nodules being difficult to detect accurately. Secondly, aiming at the problem that the characteristics of lung nodules are blurred and difficult to detect due to the continuous downsampling of the model, a multi-scale receptive field (MSRF) module is proposed and designed to improve the model's extraction of channel features. Finally, efficient omni-dimensional convolution (EODConv) is used to improve the ability of the network to extract the space, filters, and channels of the convolution kernel. (Results) Experiments were carried out on the public Luna16 dataset, and the results showed that our mAP, precision, and recall rate reached 95.26 %, 95.41 %, and 94.02 %, respectively, surpassing many state-of-the-art models. (Conclusion) In this study, a YOLOv7-based method is proposed for detecting lung nodules. Experimental results show that the proposed modification can significantly improve detection performance and is more suitable for clinical medical diagnosis.