# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 1 Introduction  
According to the World Health Organization (WHO), lung cancer is the leading cause of cancer deaths in 2020 (1.80 million deaths) [1]. The estimated number of new cases has risen to 2.89 million, and the number of deaths may reach 2.45 million worldwide by 2030 [2]. These deaths could be avoidable by an early diagnosis with a proper treatment plan. The National Lung Screening Trial (NLST) showed that the mortality of lung cancer is reduced by 20% by emphasizing the significance of nodule detection and assessment [3]. Many studies have shown the efficacy of computed tomography (CT) screening for lung cancer diagnosis and the detection of subsold nodules, as well as suspected/unsuspected lung cancer nodules [4]. CT imaging technology helps make an efficient investigation to discover pulmonary nodules. CT imaging technology generates a hundred images of the lung within a second by a single scan. It is difficult for radiologists to manually detect and segment the lung nodules from such a high number of images. In this context, computer-aided diagnosis (CAD) systems have assisted radiologists in the automated diagnosis of lung cancer and pulmonary diseases over the last several years. The authors of [5] noted that the use of an accurate lung nodule CAD system accelerates the entire diagnosis and radiotherapy process, such that patients can perform the required radiation or photon therapy on the same day. CAD systems mainly depend on the detection and segmentation of various pulmonary parts. Computer-aided detection (CADe) systems identify the region of interest(ROI) in the lung nodule, while computer-aided segmentation (CASe) systems segment the nodule region and determine its boundaries. Automated analysis of lung CT images is essential to measure the properties of lung nodules for identifying malignancy in a tumor. Lung nodule segmentation systems can determine malignancy by analyzing nodule size, shape, and change rate [6]. Although many automated nodule detection/segmentation systems have been presented in the last years [7; 8; 9], their accuracies are not high due to several challenges, such as the heterogeneity of CT images and the variation present in nodule size, shape, and location. In the last years, deep convolutional neural networks (CNNs) have been widely used to handle the lung nodule detection and segmentation problem, achieving promising results [7; 8; 9; 10; 11; 12; 13]. CNNs can learn complex features to detect and segment the nodule accurately. However, existing nodule segmentation systems use deep learning models to segment nodules from the whole-input CT images. This reduces the segmentation precision because the input images are usually resized before feeding into the deep learning model. Such resizing processes yield artifacts that badly affect the objects' boundaries and details. In an attempt to handle the challenges that accompany the automated segmentation of lung nodules, in this article, we propose the AWEU-Net method for a lung nodule detection and segmentation system based on deep learning. AWEU-Net is a fully automated deep learning-based framework that includes two cascaded stages. In the first stage, AWEU-Net automatically detects lung nodules based on a fine-tuned Faster R-CNN model. In the second stage, AWEU-Net automatically delineates lung nodules from the ROI which results from the first stage based on a U-Net architecture with two powerful blocks via position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE). Both blocks help model the correlation between the spatial and channel features and encourage the CNN to learn the most relevant features that enhance its ability to discriminate between nodule and non-nodule feature representations.