Abstract

Info

Title:: Detection of pulmonary nodules based on improved VGG-16 convolution neural network

Author(s):: CAO Yu1; XING Suxia1; PANG Jianliang2; WANG Xiaoyi1; WANG Yu1; PAN Ziyan1; SHEN Nan1; 1. School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China 2. Air Force Medical Center, Beijing 100048, China

Keywords:: Keywords: pulmonary nodule VGG-16 extreme learning machine convolutional neural network

Abstract:: Abstract: Aiming at the complex features of pulmonary nodules and the difficulties of extracting features manually, a pulmonary nodule detection model based on improved VGG-16 convolution neural network is proposed. Firstly, the lung parenchyma is obtained by threshold segmentation and mask operation after processing the image of the maximum connected area. Then, the serial number of each connected area is labeled by Regionprops for obtaining all suspected nodules. Kernel extreme learning machine instead of Softmax function is taken as classifier in VGG-16 architecture. Finally, the improved VGG-16 model is used to remove false positive nodules and complete the detection of pulmonary nodules. The proposed method is tested on LIDC-IDRI dataset, and the results showed that the improved model can achieve an accuracy of 92.56% and a sensitivity up to 94.44%. The proposed model can be used to assist doctors in the diagnosis of pulmonary nodules, and has a certain clinical value.