结合深度残差网络的SSD肺部结节检测方法

SSD Lung Nodule Detection Method Combined with Deep Residual Network

为了弥补传统的SSD算法在小目标检测中的不足,提出一种结合深度残差网络的SSD目标检测算法,用于医学影像诊断中肺结节小目标的检测识别。具体操作中,首先对肺部CT图像的切片进行预处理操作得到肺实质,通过得到的大肺实质样本对提出的方法进行训练。实验结果表明,与传统的SSD算法相比,提出的方法模型检测的敏感度为84.25%,假阳性率为10.55%,分别比传统的SSD算法在敏感度上提高了6.9%,假阳性率降低了2.7%。

In order to make up for the shortcomings of traditional SSD algorithm in small target detection,this paper combines SSD target detection algorithm with deep residual network to form a new model for detecting lung nodules of small targets.First,pre-processing the slices of lung CT images to obtain the lung parenchyma,and then input a large number of lung parenchyma samples into the model for training.Finally,the sensitivity of the model test was 84.25%,and the false positive rate was 10.55%.Compared with the traditional SSD algorithm,the sensitivity is increased by 6.9%,and the false positive rate is reduced by 2.7%.