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. 2021 Dec 16;20(1):1. doi: 10.1007/s10723-021-09596-6

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

Shui-Hua Wang 1,#, Suresh Chandra Satapathy 2,#, Qinghua Zhou 3,#, Xin Zhang 4,✉, Yu-Dong Zhang 3,✉
PMCID: PMC8674408  PMID: 34931118

Abstract

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

Keywords: Secondary pulmonary tuberculosis, pseudo-Zernike moment, Sparse autoencoder, Machine learning, Deep learning

Acknowledgments

This study was 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); British Heart Foundation Accelerator Award, UK; Sino-UK Industrial Fund, UK (RP202G0289); Global Challenges Research Fund (GCRF), UK (P202PF11).

Data Availability

The data is available upon reasonable request to the corresponding authors.

Declarations

Conflict of Interest

We have no conflicts of interest to disclose concerning this paper.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shui-Hua Wang, Suresh Chandra Satapathy and Qinghua Zhou contributed equally to this work.

Contributor Information

Shui-Hua Wang, Email: shuihuawang@ieee.org.

Suresh Chandra Satapathy, Email: sureshsatapathy@ieee.org.

Qinghua Zhou, Email: qz105@le.ac.uk.

Xin Zhang, Email: 973306782@qq.com.

Yu-Dong Zhang, Email: yudongzhang@ieee.org.

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Data Availability Statement

The data is available upon reasonable request to the corresponding authors.

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