Abstract: According to statistics, lung cancer is one of the diseases with the highest morbidity and mortality rate in the world. With the maturity of computer-aided diagnosis (CAD) and convolutional neural networks (CNN), the diagnosis and treatment in medical field are becoming more and more intelligent. In this paper, an automatic detection method of lung nodules based on target detection algorithm is presented, and a set of image processing flow of CT of lung parenchyma is presented, which combines threshold segmentation algorithm with digital morphological processing. After training and learning 1186 lung nodules in LUNA16 data set, we observe the evaluation results of YOLO V3 model in the data set to verify the model. The accuracy of the experimental results is 92.18%, the average detection time of each image is 0.035 seconds. In order to verify the validity of YOLO V3 model, this paper compares it with existing algorithms such as SSD, CNN, U-Net and so on. At the same time, this paper designs an auxiliary diagnosis system of pulmonary nodules based on CAD technology, realizes the human-computer interaction, and provides a simple and clear auxiliary diagnosis tool for doctors.

Key words: pulmonary nodule detection, target detection algorithm, YOLO V3, lung parenchyma image segmentation, CAD