Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Shui Hua Wang, Vishnu Govindaraj, Juan Manuel Gorriz*, Xin Zhang*, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


Aim: We propose a novel graph rank-based average pooling neural network (GRAPNN) to detect secondary pulmonary tuberculosis patients via chest CT imaging. Methods: First, we propose a novel rank-based pooling neural network (RAPNN) to learn the individual image-level features from chest CT images. Second, we integrate the graph convolutional network (GCN), which learns relation-aware representation among the batch of chest CT images, to RAPNN. Third, we build a novel Graph RAPNN (GRAPNN) model based on the previous integration via k-means clustering and k-nearest neighbors’ algorithm. Besides, an improved data augmentation is utilized to handle overfitting problem. Grad-ACM is used to make this GRAPNN model explainable. Results: This proposed GRAPNN method is compared with seven state-of-the-art algorithms. The results showed GRAPNN model yields the best performances with a sensitivity of 94.65%, a specificity of 95.12%, a precision of 95.17%, an accuracy of 94.88%, and an F1 score of 94.87%. Conclusions: Our GRAPNN is superior to other seven state-of-the-art approaches. The explainable mechanism in our method can identify the lesions of important lung parts (tuberculosis cavities and surrounding small lesions) for transparent decision.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
Publication statusAccepted/In press - 2021
Externally publishedYes


  • Artificial intelligence
  • Convolutional neural network
  • Deep learning
  • Graph convolutional network
  • Machine learning
  • Secondary pulmonary tuberculosis


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