  Lung cancer is deadly cancer that causes millions of deaths every year around
the world. Accurate lung nodule detection and segmentation in computed
tomography (CT) images is the most important part of diagnosing lung cancer in
the early stage. Most of the existing systems are semi-automated and need to
manually select the lung and nodules regions to perform the segmentation task.
To address these challenges, we proposed a fully automated end-to-end lung
nodule detection and segmentation system based on a deep learning approach. In
this paper, we used Optimized Faster R-CNN; a state-of-the-art detection model
to detect the lung nodule regions in the CT scans. Furthermore, we proposed an
attention-aware weight excitation U-Net, called AWEU-Net, for lung nodule
segmentation and boundaries detection. To achieve more accurate nodule
segmentation, in AWEU-Net, we proposed position attention-aware weight
excitation (PAWE), and channel attention-aware weight excitation (CAWE) blocks
to highlight the best aligned spatial and channel features in the input feature
maps. The experimental results demonstrate that our proposed model yields a
Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34%
and 83.21% on the publicly LUNA16 and LIDC-IDRI datasets, respectively.
