TBNet: a context-aware graph network for tuberculosis diagnosis

Si Yuan Lu, Shui Hua Wang, Xin Zhang, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Background and objective: Tuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT images Methods: Traditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normal Results: The proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experiments Conclusions: Our TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.

Original languageEnglish
Article number106587
JournalComputer Methods and Programs in Biomedicine
Volume214
DOIs
Publication statusPublished - Feb 2022
Externally publishedYes

Keywords

  • computed tomography
  • computer-aided diagnosis
  • graph neural network
  • random vector functional-link net
  • tuberculosis

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