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Article

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

1
Department of Biostatistics and Computing, Yonsei University Graduate School, Seoul 03722, Korea
2
The Institute for Occupational Health, Yonsei University College of Medicine, Seoul 03722, Korea
3
Division of Biostatistics, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul 03722, Korea
4
Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Korea
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(2), 250; https://doi.org/10.3390/ijerph16020250
Received: 27 November 2018 / Revised: 4 January 2019 / Accepted: 9 January 2019 / Published: 16 January 2019
(This article belongs to the Special Issue Deep Learning Methods for Healthcare)
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. View Full-Text
Keywords: deep learning; image; computer-assisted diagnosis; tuberculosis; convolutional neural network deep learning; image; computer-assisted diagnosis; tuberculosis; convolutional neural network
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MDPI and ACS Style

Heo, S.-J.; Kim, Y.; Yun, S.; Lim, S.-S.; Kim, J.; Nam, C.-M.; Park, E.-C.; Jung, I.; Yoon, J.-H. Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data. Int. J. Environ. Res. Public Health 2019, 16, 250. https://doi.org/10.3390/ijerph16020250

AMA Style

Heo S-J, Kim Y, Yun S, Lim S-S, Kim J, Nam C-M, Park E-C, Jung I, Yoon J-H. Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data. International Journal of Environmental Research and Public Health. 2019; 16(2):250. https://doi.org/10.3390/ijerph16020250

Chicago/Turabian Style

Heo, Seok-Jae, Yangwook Kim, Sehyun Yun, Sung-Shil Lim, Jihyun Kim, Chung-Mo Nam, Eun-Cheol Park, Inkyung Jung, and Jin-Ha Yoon. 2019. "Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers’ Health Examination Data" International Journal of Environmental Research and Public Health 16, no. 2: 250. https://doi.org/10.3390/ijerph16020250

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