【摘要】 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%。更多还原

【Abstract】 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%.更多还原