## 1 Introduction  
According to a report from the World Health Organization in 2018, there were about 9.6 million deaths from cancer globally, of which 1.76 million cases were attributed to lung cancers [1]. Studies have identified environmental factors and smoking as major causes of lung cancer [2]. Generally, chest X-ray, computed tomography (CT) and magnetic resonance imaging are modalities used to evaluate lung cancer [3, 4]. The chest X-ray is the first test in diagnosing lung cancer. It indicates abnormal formations in the lungs. Compared to a chest X-ray, a CT scan can show a more detailed view of the lungs and can also show the exact shape, size, and location of formations. A CT scan is therefore a major diagnostic tool for the assessment of lung cancer. To reduce the workload of analyzing CT images manually and to avoid subjective interpretations, machine learning techniques are applied to computer-aided design systems for objectively auxiliary diagnosis. Lately, due to the rapid growth of deep learning, convolutional neural networks (CNNs) not only show good performance in image classification and object detection tasks [5, 6, 7], but are also widely used in several applications, including smart homes, driverless cars, manufacturing robots, drones, and chat robots. The studies of CNNs have been continually innovating and improving. In 1998, [PERSON] et al. proposed LeNet-5 [8], a simple CNN for handwritten digits classification. The LeNet-5 comprises a feature extraction part convolutional layers and pooling layers and a classification part fully connected layer. Subsequently, in 2012, [PERSON] et al. proposed AlexNet [9] and won the ImageNet Large Scale Visual Recognition Competition. AlexNet replaces Sigmoid and Tanh activation function with a rectified linear unit. It also introduces Dropout and max pooling which are different from LeNet-5. In 2014, [PERSON] et al. proposed GoogLeNet [10] and proposed Inception module which uses three different size of convolutional kernels simultaneously for extracting more features in one layer. In the same year, [PERSON] et al. proposed VGGNet model [11]. VGGNet adopts \(3\times 3\) stacked convolutional layers and deepening the depth of the network as well as the number of input and output channel of layers. In 2015, [PERSON] et al. proposed ResNet [12] and introduced a residual block alleviating the degradation problem of the deep network. Certainly, more and more architectures are innovated. However, unbalanced or sparse data sets and network parameter settings for training are two major problems faced by deep learning. Unbalanced data, especially medical images, is one of the most challenging tasks in deep learning [13; 14; 15; 16; 17]. Typical data augmentation methods include translation, rotation, flipping, and zooming [18; 19]. However, those geometric transformations might not be able to provide sufficient data diversity. In 2014, generative adversarial networks (GANs) [20] were proposed to tackle the problem of sparse data. This model consists of two networks: one generator network and one discriminator network. The generator network aims to generate plausible fake images. On the other hand, the discriminator network distinguishes real data from the data created by the generator or real data and acts as a classifier. In 2015, deep convolutional GANs (DCGANs), a direct extension of GANs, were proposed [21] which replaced the original convolutional layers by transposed convolutional layers. Later, many studies discussed the complementary data process techniques in medical applications. [PERSON] et al. [22] investigated the impact of 13 data augmentation scenarios, such as traditional color and geometric transforms, elastic transforms, random erasing, and lesion mixing method for melanoma classification. The results confirmed that data augmentation can lead to more performance gains than obtaining new images. [PERSON] et al.