JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2018, Vol. 25 ›› Issue (6): 1-6.doi: 10.19682/j.cnki.1005-8885.2018.1022

• Artificial intelligence •     Next Articles

Pulmonary tuberculosis detection model of chest X-ray images using convolutional neural network

He Jin, Wang Cong, Chen Zhao   

  1. 1. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, China
    3. China Electronic Data Service CO. , LTD, Beijing 100872, China
  • Received:2018-08-07 Revised:2018-12-31 Online:2018-12-30 Published:2019-02-26
  • Contact: He Jin, E-mail:
  • About author:He Jin, E-mail:
  • Supported by:
    This workwas supported by the National Key Research and Development Program of China (2017YFC1307705).

Abstract: The primary screening for pulmonary tuberculosis mainly relies on X-ray imaging all over the world. In recent years, the incidence of pulmonary tuberculosis has rebounded. This paper proposes a convolutional neural networks (CNN) based model on the tuberculosis detection of chest X-ray images, which is used for the automatic screening of pulmonary tuberculosis. Compared with the conventional CNN, this model can be used to detect the details of images and the areas of the disease quickly and accurately. There is an improvement in the learning speed and accuracy rate of our method, so it can better complete the work of anomaly detection and it can provide more effective auxiliary decision information for the practitioners.

Key words: X-ray images, pulmonary tuberculosis, CNN, automatic screening

CLC Number: