Detecting TB With Deep Convolutional Neural Networks

The best-performing classifier had an area under the curve of 0.99.
The best-performing classifier had an area under the curve of 0.99.

HealthDay News — Deep learning with deep convolutional neural networks (DCNNs) can result in accurate detection of tuberculosis (TB) on chest radiographs, according to a study published in Radiology.

Paras Lakhani, MD, and Baskaran Sundaram, MD, from Thomas Jefferson University Hospital in Philadelphia, and colleagues used 4 datasets that consisted of 1007 posteroanterior chest radiographs to examine the efficacy of DCNNs for detecting TB. 

The datasets were split into training, validation, and test (68%, 17.1%, and 14.9%%, respectively). AlexNet and GoogLeNet DCNNs were used to classify the images as having manifestations of pulmonary TB or as healthy. They used untrained and pretrained networks on ImageNet and performed augmentation with multiple preprocessing techniques. An independent cardiothoracic radiologist interpreted images in cases where the classifiers were in disagreement.

The researchers found that the best-performing classifier had an area under the curve (AUC) of 0.99, which was an ensemble of AlexNet and GoogLeNet DCNNs. The AUCs were greater for pretrained vs untrained models (P <.001). Accuracy was increased by augmenting the dataset further (P values, 0.03 and 0.02 for AlexNet and GoogLeNet, respectively). In 13 of 150 test cases, the DCNNs had disagreement; the cardiothoracic radiologist correctly interpreted all cases. The radiologist-augmented approach resulted in sensitivity and specificity of 97.3% and 100%, respectively.

"Deep learning with DCNNs can accurately classify TB at chest radiography with an AUC of 0.99," the researchers write.

Reference

Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks [published online April 24, 2017]. Radiology. doi: 10.1148/radiol.2017162326

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