  Recent studies have shown that lung cancer screening using annual low-dose
computed tomography (CT) reduces lung cancer mortality by 20% compared to
traditional chest radiography. Therefore, CT lung screening has started to be
used widely all across the world. However, analyzing these images is a serious
burden for radiologists. The number of slices in a CT scan can be up to 600.
Therefore, computer-aided-detection (CAD) systems are very important for faster
and more accurate assessment of the data. In this study, we proposed a
framework that analyzes CT lung screenings using convolutional neural networks
(CNNs) to reduce false positives. We trained our model with different volume
sizes and showed that volume size plays a critical role in the performance of
the system. We also used different fusions in order to show their power and
effect on the overall accuracy. 3D CNNs were preferred over 2D CNNs because 2D
convolutional operations applied to 3D data could result in information loss.
The proposed framework has been tested on the dataset provided by the LUNA16
Challenge and resulted in a sensitivity of 0.831 at 1 false positive per scan.
