Classification model of benign and malignant nodules in lung CT images based on deep learning of residual network

被引：0
作者：
Lin Z. [1 ]
Wang G. [1 ]
Chen J. [2 ]
Fu Q. [1 ]
机构：
[1] School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou
[2] Guangzhou Cangheng Automatic Control Technology Co., Ltd., Guangzhou
来源：
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2020年 / 41卷 / 03期
关键词：
Benign and malignant classification; Convolutional neural network; Deep learning; Pulmonary nodule; Residual network;
D O I：
10.19650/j.cnki.cjsi.J1905600
中图分类号：
学科分类号：
摘要：
Computer aided diagnosis of benign and malignant pulmonary nodules plays a significant role in timely treatment of lung cancer. Aiming at the current situation that the diagnosis accuracy of benign and malignant pulmonary nodules in computer aided diagnosis system is low and the misdiagnosis rate and wrong diagnosis rate are relatively high, a classification model of lung benign and malignant nodules based on residual network is proposed. Firstly, partial lung CT images (totally 10 402) from LIDC-IDRI are selected as a data set, the data are augmented by horizontal flipping the images and then the images are converted into single channel images. Afterwards, cropping and normalization processing is performed. Finally, the data are divided into training set and test set (7: 3), and used to train and test the designed residual network (ResNet-26). After training, the obtained test results are as follows: the accuracy rate of classification, sensitivity and specificity of benign and malignant pulmonary nodules are 97.53%, 97.91% and 97.18%, respectively; and the calculated AUC is 0.958. Comparison results show that in terms of accuracy, sensitivity and specificity specifications, the proposed method is superior to existing various other methods, the classification result can provide good assistance in diagnosis for the doctors. © 2020, Science Press. All right reserved.
引用
收藏
页码：248 / 256
页数：8
相关论文
共 23 条
  • [11] LIN L, WU Y CH, SONG SH, Et al., Lung image database consortium and image database resource initiative: Reviewing progresses toward improved computer-aided diagnosis of pulmonary nodule, Chinese Medical Equipment Journal, 39, 10, pp. 95-99, (2018)
  • [12] LECUN Y, BENGIO Y., Convolutional networks for images, speech, and time series, The Handbook of Brain Theory and Neural Networks, (1998)
  • [13] ZHOU F Y, JIN L P, DONG J., Review of convolutional neural network, Chinese Journal of Computers, 40, 6, pp. 1229-1251, (2017)
  • [14] WANG Y, YU Z J, ZHU L Q, Et al., Fast feature extraction algorithm for high-speed railway clearance intruding objects based on CNN, Chinese Journal of Scientific Instrument, 38, 5, pp. 1267-1275, (2017)
  • [15] YANG L J, CAO H., Deep learning based weld and flange identification in pipeline, Chinese Journal of Scientific Instrument, 39, 2, pp. 193-202, (2018)
  • [16] KRIZHEVSKY A, SUTSKEVER I, HINTON G., ImageNet classification with deep convolutional neural networks, NIPS: Conference and Workshop on Neural Information Processing Systems, pp. 1097-1105, (2012)
  • [17] SIMONYAN K, ZISSERMAN A., Very deep convolutional networks for large-scale image recognition, Computer Science, (2014)
  • [18] SZEGEDY C, LIU W, JIA Y, Et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, (2015)
  • [19] GUO Y X, YANG W, LIU Q, Et al., Survey of residual network, Application Research of Computers, pp. 1-8, (2019)
  • [20] HE K, ZHANG X, REN S, Et al., Deep residual learning for image recognition, 2016 CVPR: 2016 Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016)
123