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Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data

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Listed:
  • Seok-Jae Heo

    (Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul 03722, Korea)

  • Yangwook Kim

    (The Institute for Occupational Health, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Sehyun Yun

    (The Institute for Occupational Health, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Sung-Shil Lim

    (The Institute for Occupational Health, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Jihyun Kim

    (The Institute for Occupational Health, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Chung-Mo Nam

    (Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul 03722, Korea
    Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Eun-Cheol Park

    (Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Inkyung Jung

    (Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Jin-Ha Yoon

    (The Institute for Occupational Health, Yonsei University College of Medicine, Seoul 03722, Korea
    Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Korea)

Abstract

We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers’ health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic variables (D-CNN). The I-CNN and D-CNN models were trained on 1000 chest X-ray images, both positive and negative, for tuberculosis. Feature extraction was conducted using VGG19, InceptionV3, ResNet50, DenseNet121, and InceptionResNetV2. Age, weight, height, and gender were recorded as demographic variables. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated for model comparison. The AUC values of the D-CNN models were greater than that of I-CNN. The AUC values for VGG19 increased by 0.0144 (0.957 to 0.9714) in the training set, and by 0.0138 (0.9075 to 0.9213) in the test set (both p < 0.05). The D-CNN models show greater sensitivity than I-CNN models (0.815 vs. 0.775, respectively) at the same cut-off point for the same specificity of 0.962. The sensitivity of D-CNN does not attenuate as much as that of I-CNN, even when specificity is increased by cut-off points. Conclusion: Our results indicate that machine learning can facilitate the detection of tuberculosis in chest X-rays, and demographic factors can improve this process.

Suggested Citation

  • Seok-Jae Heo & Yangwook Kim & Sehyun Yun & Sung-Shil Lim & Jihyun Kim & Chung-Mo Nam & Eun-Cheol Park & Inkyung Jung & Jin-Ha Yoon, 2019. "Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data," IJERPH, MDPI, vol. 16(2), pages 1-9, January.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:2:p:250-:d:198313
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    References listed on IDEAS

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    1. Jinseok Kim & Jenna Kim, 2018. "The impact of imbalanced training data on machine learning for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 511-526, October.
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