%0 Journal Article
%A Chen Zhao
%A He Jin
%A Wang Cong
%T Pulmonary tuberculosis detection model of chest X-ray images using convolutional neural network
%D 2018
%R 10.19682/j.cnki.1005-8885.2018.1022
%J Journal of China Universities of Posts and Telecommunications
%P 1-6
%V 25
%N 6
%X 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.
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2018.1022