Lung nodule detection algorithm using improved YOLOv7 network model / 中国医学物理学杂志

ABSTRACT

To address the issues in the current lung nodule detection for tuberculosis where the existing object detection algorithms have limited precision for small nodules and often predict bounding box locations inaccurately,a lung nodule detection method based on YOLOv7 is presented for obtaining small lung nodules more effectively and realizing the continuous convergence of target detection box.Based on the framework of YOLOv7 network model,the improvements are made in the following 3 aspects.(1)The cross-channel information and target airspace information are obtained with the effective SimAM channel attention mechanism embed in the Head network,so as to highlight the target features and enable the model to identify the regions of interest more accurately.(2)SIOU boundary loss function is used to increase the angle cost on the original loss function,and redefine the distance cost and shape cost to improve the convergence rate and reduce the loss value.(3)SIOU-NMS is used to replace the non-maximum suppression algorithm for reducing the error suppression due to target occlusion.The results of experiments on a custom lung nodule dataset show that compared with the original YOLOv7,the proposed method improves accuracy and recall rate by 2.9%and 3.1%,and the mean average precision at a confidence threshold of 0.5 is increased by 3.7%.The model can effectively assist in the diagnosis of lung nodules.