Abstract
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.
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Acknowledgement
This paper is partially supported by Royal Society International Exchanges Cost Share Award, UK (RP202G0230); Medical Research Council Confidence in Concept Award, UK (MC_PC_17171); Hope Foundation for Cancer Research, UK (RM60G0680); MINECO/JUNTA/FEDER, Spain/regional/Europe (RTI2018-098913-B100, CV2045250, A-TIC-080-UGR18); British Heart Foundation Accelerator Award, UK
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Wang, SH., Govindaraj, V., Gorriz, J.M. et al. Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-02998-0
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DOI: https://doi.org/10.1007/s12652-021-02998-0