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

Authors
 Seok-Jae Heo  ;  Yangwook Kim  ;  Sehyun Yun  ;  Sung-Shil Lim  ;  Jihyun Kim  ;  Chung-Mo Nam  ;  Eun-Cheol Park  ;  Inkyung Jung  ;  Jin-Ha Yoon 
Citation
 International Journal of Environmental Research and Public Health, Vol.16(2) : E250, 2019 
Journal Title
 International Journal of Environmental Research and Public Health 
ISSN
 1661-7827 
Issue Date
2019
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.
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DOI
10.3390/ijerph16020250
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Preventive Medicine and Public Health (예방의학교실) > 1. Journal Papers
4. Graduate School of Public Health (보건대학원) > Graduate School of Public Health (보건대학원) > 1. Journal Papers
Yonsei Authors
김지현(Kim, Ji Hyun)
남정모(Nam, Jung Mo) ORCID logo https://orcid.org/0000-0003-0985-0928
박은철(Park, Eun-Cheol) ORCID logo https://orcid.org/0000-0002-2306-5398
윤세현(Yun, Sehyun)
윤진하(Yoon, Jin Ha) ORCID logo https://orcid.org/0000-0003-4198-2955
정인경(Jung, Inkyung) ORCID logo https://orcid.org/0000-0003-3780-3213
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/167752
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