Abstract
BACKGROUND: In developing countries where both high rates of smoking and endemic tuberculosis (TB) are often present, identification of early lung cancer can be significantly confounded by the presence of nodules such as those due to latent TB (LTB). It is very challenging to distinguish lung cancer and LTB without invasive procedures, which have their own risks of morbidity and even mortality. METHODS: Our method uses a customized VGG16-based 15-layer 2-dimensional deep convolutional neural network (DNN) architecture with transfer learning. The DNN was trained and tested on sets of CT images set extracted from the National Lung Screening Trial and the National Institute of Allergy and Infectious Disease TB Portals. Performance of the DNN was evaluated under locked and step-wise unlocked pretrained weight conditions. RESULTS: The DNN with unlocked pretrained weights achieved an accuracy of 90.4% with an F score of 90.1%. CONCLUSIONS: Our findings support the potential for a DNN to serve as a noninvasive screening tool capable of reliably detecting and distinguishing between lung cancer and LTB.