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. 2019 Jan 16;16(2):250.
doi: 10.3390/ijerph16020250.

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

Seok-Jae Heo  1 , Yangwook Kim  2 , Sehyun Yun  3 , Sung-Shil Lim  4 , Jihyun Kim  5 , Chung-Mo Nam  6   7 , Eun-Cheol Park  8 , Inkyung Jung  9 , Jin-Ha Yoon  10   11
Affiliations

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

Seok-Jae Heo et al. Int J Environ Res Public Health. .

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: computer-assisted diagnosis; convolutional neural network; deep learning; image; tuberculosis.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Lung segmentation using U-Net before training the convolutional neural network: (a) the original chest X-ray image, (b) a mask of lung structures segmented through U-Net, and (c) the final segmented image of the lungs.
Figure 2
Figure 2
Flowchart of tuberculosis classification using the convolutional neural network (CNN) model. Flow 1 uses only chest X-rays for tuberculosis classification. Flow 2 uses demographic variables as well as chest X-rays.
Figure 3
Figure 3
Value of sensitivity and specificity changed according to the cut-off point: (a) the sensitivity and specificity for the training data set and (b) the sensitivity and specificity for the test data set.
See this image and copyright information in PMC

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