Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs.

Journal: Journal of healthcare engineering
Published Date: Aug 24, 2020

Abstract

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

Authors

  • Yilin Xie
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Zhuoyue Wu
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Xin Han
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Hongyu Wang
    School of Information Science and Technology, Northwest University, Xi'an, Shaanxi, China.
  • Yifan Wu
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Lei Cui
    School of Information Science and Technology, Northwest University, Xi'an, Shaanxi, China.
  • Jun Feng
    Linping Hospital of Integrated Traditional Chinese and Western, Medicine, Hangzhou, Zhejiang, China.
  • Zhaohui Zhu
    Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China. zhuzhaohui316@163.com.
  • Zhongyuanlong Chen
    Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC, Urumqi, Xinjiang Uygur Autonomous Region 830049, China.

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