Classification of Chest X-ray Disease Based on Convolutional Neural Network

Authors

Huang Xin, Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
Fang Yu, Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;
Mengdan Gu, Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China;

Abstract

Abstract: The artificial intelligence technology can effectively assist the chest X-ray diagnosis. On the basis of the analysis of Chinese reports of chest X-rays, a labeling method of the thoracic disease classification for the chest abnormal parts is proposed and a dataset of the thoracic disease classification labels is complied. The thoracic disease classification is evaluated through four kinds of convolutional neural networks, AlexNet, VGGNet, ResNet and DenseNet and through three kinds of training methods, direct training, ImageNet pre-training and Chest X-14 pre-training. The result shows that the more complicated convolutional neural network with the more parameters, the better performance in obtaining the key information from the chest X-ray images can be. The model pre-trained by Chest X-14, a large dataset of chest X-ray, has the better result than the other pre-training methods.

Recommended Citation

Xin, Huang; Yu, Fang; and Gu, Mengdan (2020) "Classification of Chest X-ray Disease Based on Convolutional Neural Network," Journal of System Simulation: Vol. 32: Iss. 6, Article 22.
DOI: 10.16182/j.issn1004731x.joss.18-0712
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss6/22

First Page

1188

Revised Date

2019-03-11

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.18-0712

Last Page

1194

CLC

TP391

Recommended Citation

Huang Xin, Fang Yu, Gu Mengdan. Classification of Chest X-ray Disease Based on Convolutional Neural Network[J]. Journal of System Simulation, 2020, 32(6): 1188-1194.

DOI

10.16182/j.issn1004731x.joss.18-0712