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

Seok-Jae Heo, Yangwook Kim, Sehyun Yun, Sung-Shil Lim, Jihyun Kim, Chung-Mo Nam, Eun-Cheol Park, Inkyung Jung and Jin-Ha Yoon
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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
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

IJERPH, 2019, vol. 16, issue 2, 1-9

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.

Keywords: deep learning; image; computer-assisted diagnosis; tuberculosis; convolutional neural network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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