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.
Highlights
X-rays represent the most basic form of radiography, and are often considered the first step in medical examinations of organs and structures surrounding the chest [1]
We aimed to test the performance of convolutional neural networks (CNNs) on detecting tuberculosis and evaluate the difference in performance between a CNN based on images only (I-CNN) and a CNN that includes demographic variables (D-CNN) for the classification of tuberculosis in chest X-ray images
We evaluated the difference in performance when classifying tuberculosis between a CNN based only
Summary
X-rays represent the most basic form of radiography, and are often considered the first step in medical examinations of organs and structures surrounding the chest [1]. Chest X-rays may provide insight into the patient’s condition, as certain diseases are associated with heart and lung abnormalities. In certain situations, physicians other than radiologists may have difficulty making accurate diagnoses based solely on images. For almost 60 years, researchers have devoted substantial effort to developing methods for computer-aided diagnosis (CAD) [2]. Research regarding convolutional neural networks (CNNs) for CAD has expanded to include chest X-rays, computed tomography (CT), and high-resolution CT (HR-CT) [3]. Res. Public Health 2019, 16, 250; doi:10.3390/ijerph16020250 www.mdpi.com/journal/ijerph
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