Two-stage classification of tuberculosis culture diagnosis using convolutional neural network with transfer learning

Abstract

Tuberculosis (TB) has been one of top 10 leading causes of death. A computer-aided diagnosis system to accelerate TB diagnosis is crucial. In this paper, we apply convolutional neural network and deep learning to classify the images of TB culture test—the gold standard of TB diagnostic test. Since the dataset is small and imbalanced, a transfer learning approach is applied. Moreover, as the recall of non-negative class is an important metric for this application, we propose a two-stage classification method to boost the results. The experiment results on a real dataset of TB culture test (1727 samples with 16,503 images from Tao-Yuan General Hospital, Taiwan) show that the proposed method can achieve 99% precision and 98% recall on the non-negative class.

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Acknowledgements

This paper is partially supported by MOST Taiwan under grants 108-2410-H-002-230-MY2 and 108-3116-F-002-003-CC1. We thank Dr. H.C. Chen in Tao-Yuan General Hospital, Ministry of Health and Welfare, Taiwan, for providing the data.

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Correspondence to Jeng-Wei Lin.

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Chang, RI., Chiu, YH. & Lin, JW. Two-stage classification of tuberculosis culture diagnosis using convolutional neural network with transfer learning. J Supercomput 76, 8641–8656 (2020). https://doi.org/10.1007/s11227-020-03152-x

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Keywords

  • Automatic tuberculosis diagnosis
  • Tuberculosis culture test
  • Deep learning
  • Transfer learning
  • Multi-stage classification