SECONDARY PULMONARY TUBERCULOSIS RECOGNITION BY ROTATION ANGLE VECTOR GRID-BASED FRACTIONAL FOURIER ENTROPY

Shui Hua Wang, Yeliz Karaca, Xin Zhang, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Article number2240047
JournalFractals
Volume30
Issue number1
DOIs
Publication statusPublished - 1 Feb 2022
Externally publishedYes

Keywords

  • Deep Learning
  • Deep Stacked Sparse Autoencoder
  • Fractional Fourier Entropy
  • Fractional Fourier Transform
  • Multiple-Way Data Augmentation
  • Recognition
  • Rotation Angle Vector Grid
  • Secondary Pulmonary Tuberculosis

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