Abstract

Info

Author(s):: WANG Qianliang1; 2; SHI Hongli1; 2; 1. School of Biomedical Engineering, Capital Medical University, Beijing 100069, China 2. Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China

Keywords:: deep learning pulmonary nodule YOLO V3 convolutional neural network

Abstract:: In view of small proportion of pulmonary nodules in CT images, irregular shapes of pulmonary nodules and unsatisfactory results obtained by the direct application of YOLO V3 algorithm for pulmonary nodule detection, a pulmonary nodule detection method based on improved YOLOV3 is proposed in the study. Preprocessing such as resampling and parenchymal segmentation is carried out. Then the basic network structure ofYOLOV3 is modified, and the number of structural units of the backbone network and the detection network is adjusted. Leaky ReLU activation function is replaced by Mish activation function, and receptive field block layers with dilated convolutions are added. Moreover, the loss function is modified. Finally, the improved YOLO V3 is used to detect pulmonary nodules, and the comparative experiment is completed. The proposed method is tested on LIDC-IDRI data set, and the results show that the improved YOLO V3 achieves an accuracy of 88.89% and a sensitivity of 94.73%, indicating that the proposed method can effectively detect pulmonary nodules.