Abstract：Lung cancer has always been one of the serious threats to human health. As an important sign of early lung cancer, pulmonary nodules are of great significance in the early diagnosis and treatment of lung cancer. The traditional CT image lung nodule detection method is not only cumbersome, but also slow in processing speed, and the detection rate and positioning accuracy of the nodules need to be improved. This paper proposed a CT image lung nodule detection method based on the asymmetric convolution kernel YOLO V2 network. First, the continuous CT sequence was superimposed to construct a pseudo-color data set to enhance the difference between lesion and healthy tissue, which contained asymmetric volume. The inception V3 module of the accumulation was introduced into the YOLO V2 network to construct a deep network suitable for lung nodule detection. This aspect was drawn on the advantages of the YOLO V2 network in target detection, and on the other hand through the inception V3 module. The width and depth of the network were amplified to extract more abundant features; in order to further improve the positioning accuracy of the nodules, the design and calculation method of the loss function had also been improved. In order to verify the performance of the proposed test model, CT images of 1010 cases were selected from the LIDC-IDRI data set for training and testing. In lung nodules larger than 3 mm, the detection sensitivity was 94.25%, and the false positive rate was 8.50%. Experimental results showed that the lung nodule detection method proposed in this paper not only simplified the processing of lung CT images, but also was superior to traditional methods in nodule detection rate and positioning accuracy, providing a new way for lung nodule detection.
李新征, 金炜*, 李纲, 尹曹谦. 非对称卷积核YOLO V2网络的CT影像肺结节检测[J]. 中国生物医学工程学报, 2019, 38(4): 401-408.
Li Xinzheng, Jin Wei, Li Gang, Yin Caoqian. YOLO V2 Network with Asymmetric ConvolutionKernel for Lung Nodule Detection of CT Image. Chinese Journal of Biomedical Engineering, 2019, 38(4): 401-408.
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