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Secondary Pulmonary Tuberculosis Recognition By Rotation Angle Vector Grid-Based Fractional Fourier Entropy

Author

Listed:
  • SHUI-HUA WANG

    (School of Mathematics and Actuarial Science, University of Leicester, University Road, Leicester LE1 7RH, UK)

  • YELIZ KARACA

    (��University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA)

  • XIN ZHANG

    (��Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Huai’an, Jiangsu 223002, P. R. China)

  • YU-DONG ZHANG

    (�School of Informatics, University of Leicester, University Road, Leicester LE1 7RH, UK)

Abstract

Aim: Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis bacteria. This study plans to build a novel deep learning-based model for the accurate recognition of tuberculosis. Methods: We propose a novel model — rotation angle vector grid-based fractional Fourier entropy and deep stacked sparse autoencoder (RAVG-FrFE–DSSAE) — which uses RAVG-FrFE as a feature extractor and harnesses DSSAE as the classifier. Moreover, an 18-way MDA is introduced on the training set to avoid overfitting. Results: Experimental results of 10 runs of 10-fold CV showcase that this proposed RAVG-FrFE–DSSAE algorithm yields a reasonable performance including of 93.68±1.11% sensitivity, 94.38±1.11% specificity, 94.35±1.04% precision, 94.03±0.69% accuracy, 94.01±0.70% F1-score, 88.07±1.38% MCC, 94.01±0.70% FMI, and 0.9725 AUC, respectively. Conclusions: Our result outperforms the eight state-of-the-art approaches. Besides, the result shows the effectiveness of the 18-way MDA.

Suggested Citation

  • Shui-Hua Wang & Yeliz Karaca & Xin Zhang & Yu-Dong Zhang, 2022. "Secondary Pulmonary Tuberculosis Recognition By Rotation Angle Vector Grid-Based Fractional Fourier Entropy," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(01), pages 1-17, February.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:01:n:s0218348x22400473
    DOI: 10.1142/S0218348X22400473
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