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, Jin Ha Yoon

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish
Article number250
JournalInternational journal of environmental research and public health
Volume16
Issue number2
DOIs
Publication statusPublished - 2019 Jan 2

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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