  Lung cancer is the most common form of cancer found worldwide with a high
mortality rate. Early detection of pulmonary nodules by screening with a
low-dose computed tomography (CT) scan is crucial for its effective clinical
management. Nodules which are symptomatic of malignancy occupy about 0.0125 -
0.025\% of volume in a CT scan of a patient. Manual screening of all slices is
a tedious task and presents a high risk of human errors. To tackle this problem
we propose a computationally efficient two stage framework. In the first stage,
a convolutional neural network (CNN) trained adversarially using Turing test
loss segments the lung region. In the second stage, patches sampled from the
segmented region are then classified to detect the presence of nodules. The
proposed method is experimentally validated on the LUNA16 challenge dataset
with a dice coefficient of $0.984\pm0.0007$ for 10-fold cross-validation.
