基于改进YOLOv7网络模型的肺结节检测算法

Lung nodule detection algorithm using improved YOLOv7 network model

针对当前肺结核型肺结节检测领域中目标检测算法存在的小目标检测精度不高和模型预测框定位不准的问题,提出一种基于YOLOv7的肺结节检测方法,旨在更有效地获取小肺结节并实现目标检测框的持续收敛。在YOLOv7网络模型框架下,在以下3个方面进行改进:首先对头部网络嵌入有效SimAM通道注意力机制获取跨通道信息和目标空域信息,以突出目标特征,使模型能够更加精确地识别感兴趣区域。其次采用SIOU边界损失函数,在原损失函数上增加角度成本,重新定义距离成本和形状成本,以提高收敛速度,降低损失值。最后利用SIOU-NMS替换非极大抑制算法,缓解因目标遮挡而导致错误抑制的现象。实验结果表明,在自制肺结节数据集上,改进网络模型与原YOLOv7模型相比,准确率和召回率分别提升2.9%和3.1%,置信度为0.5时平均精度均值(mAP:0.5)提高3.7%,该模型能有效辅助诊断肺结节。

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.