# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
## 1 Introduction  
Lung cancer is the cancer with the highest mortality in the world. According to statistics, 222,500 new cases of lung cancer and 155,870 deaths are predicted in the United States in 2017 [1]. Lung cancer deaths account for 25% of all cancer deaths, ranking first among all cancers. The seminal National Lung Screening Trial [2] shows that screening for high-risk lung cancer with low-dose computed tomography (CT) is more effective than chest X-ray screening, which can reduce the 7-year mortality rate by 20%. Since then, screening for lung cancer with low-dose CT has been widespread worldwide. However, a large number of CTs require more radiologists to analyze and diagnose, which greatly increases the burden and the needs of radiologists. To alleviate this, researchers have studied computer-aidedmethods for the detection of lung nodules based on CT and have proposed multiple computer-aided detection (CAD) systems for this purpose. These CAD systems can be divided into non-convolution neural network CAD systems, such as the use of a support vector machine (SVM) classifier for lung nodule detection [3] or using image intensity, shape, and texture features for nodule detection [4; 5; 6] as proposed by [PERSON] and [PERSON]; and convolution-neural-network-based CAD systems, such as lung nodule detection combined with multi-section based on a 2D convolution neural network. A typical CAD system consists of two stages: 1) nodule candidate detection and 2) false positive reduction [7]. The aim of the nodule candidate detection is to detect nodule candidates at a very high sensitivity, which usually produces many false positives. In the false positive reduction stage, the system is expected to have a good classification performance to reduce false positive nodules in the candidates at the nodule discovery stage. Finally, the probability of each candidate nodule being a true positive is given. This stage has an especially important impact on the performance of the CAD system. Due to the improvement of modern computer computing power and the development of deep learning [8], convolution neural networks are increasingly being applied in the image analysis field [9]. Because convolution neural networks have a strong ability to extract image features, CAD systems based on them have shown better performance than other methods and achieved a higher competition performance metric (CPM) score. The approach proposed by [PERSON] et al. uses two deep 3D faster R-CNN and U-Net encoder-decoders with MixNet to detect and learn the features of the lung nodules, respectively [10]. For the DeepLung model, a 3D faster region-based convolutional neural network (R-CNN) was designed for nodule detection with 3D dual path blocks and a U-NET-like encoder-decoder structure to effectively learn nodule features, and deep 3D dual path networks (DPNs) were designed for nodule detection and classification, respectively [11]. Although the existing CAD systems based on convolution neural networks have achieved better performance than CAD based on other approaches [12; 13; 14], there are still two problems in these studies: (1) Too many false positive nodules are introduced in the nodule discovery stage, and the convolution neural network is prone to false positive nodules in the subsequent false positive reduction stage. (2) These studies mostly use 2D convolution neural networks for false positive reduction. It is well-known that a CT image has three dimensions. A 2D convolution neural network cannot make full use of the 3D feature and spatial information of the image data. So, there is still much room for the precision of the network to improve. Aiming at the above problems, we use the spatial information of data as much as possible in the phases of both candidate nodule detection and false positive nodule reduction. The main contributions are as follows: (1) A nodule discovery method based on the U-NET framework [15] is designed, which is based on a 2D convolution neural network and has good feature extraction ability.