## 2 Related Works  
Since large image datasets of lung cancers are rare and deep learning methods are novel in the diagnosis of diseases, there are few researches in the diagnosis of lung cancer [8]. [PERSON] et al. compared overfeat CNN and Food and Drug Administration as a commercial method of CAD for the detection of lung cancer nodules. CT scan images of Lung Image Database Consortium (LIDC) were used for the detection of nodules. Features of lung nodules were extracted by overfeat CNN, and an SVM algorithm was used for classification of the nodules. Furthermore, the nodules were detected by commercial CAD systems. Results showed that each method could detect nodules by over 70% sensitivity [9]. [PERSON] et al. proposed CNN to classify and characterize different lung tissues of lung diseases. CT scan images of University Hospital of Geneva and Bern University Hospital were used as Interstitial Lung Disease (ILD) datasets. Their proposed CNN contained five convolutional layers, one pooling layer, and three fully connected layers. The proposed algorithm was compared to other CNN architecture e.g., LeNet, AlexNet and VGG Net. Results showed that the proposed CNN for classification and detection of tissues was superior compared to the other algorithms. The proposed CNN achieved a 85.61% accuracy rate [10]. [PERSON] and [PERSON] used DNN for classification of lung cancer nodules. CT scan images of LIDC and Image Database Resource Initiative (IDRI) were used as a dataset. Four different topologies with different numbers of convolutional layers were compared. Results demonstrated that the accuracy rate of all used methods by different convolutional layers was close to each other, and the network by five convolutional layers achieved the highest accuracy rate (82.10%) [11]. [PERSON] et al. compared three algorithms of deep learning and a traditional CAD system to diagnose lung cancer nodules on CT scan images. They used LIDC and IDRI datasets for the diagnosis of lung cancer. Deep Belief Network (DBN), CNN and Stacked Denoising Auto Encoder (SDAE) were used as three algorithms of deep learning. The results of the accuracy rates demonstrated that CNN and DBN were superior compared to the SDAE and traditional CAD methods Furthermore, DBN achieved the highest accuracy rate of nodules classification (81.19%) [8]. [PERSON] et al. applied Multi-scale CNN with multi-stream architecture as a deep learning method for the classification of lung cancer nodules on CT scan images. In order to characterize lung cancer nodules, Multicentric Italian Lung Detection (MID) and Danish Lung Cancer Screening (DLCS) datasets were used. Automatic nodules classification in six types was done without using any segmentation methods. All scales of CNN were combined in a fully connected layer of CNN. Results of proposed multi-scale CNN were compared with radiologists' diagnosis and the average accuracy rate of CNN (69.6%) is close to the average accuracy rate of radiologists (72.9%). Moreover, the accuracy of CNN with three scales was compared with SVM-based pixel intensity of patches and SVM-based unsupervised learning of features. Results show that CNN with three scales achieved a higher accuracy rate (79.5%) than the other two SVM-based methods [12]. [PERSON] et al. proposed a CNN algorithm with a single convolutional layer for the classification of patches on high-resolution computed tomography images. ILD lung dataset was used for this purpose. Furthermore, a combination of SVM classifier with three feature extraction methods (i.e., Scale Invariant Feature Transform (SIFT), Local Binary Pattern and Restricted Boltzmann Machine (RBM)) was used to extract features and classify images. Their proposed CNN was compared with the combination of a three feature extraction method and SVM classifier. Results showed that their proposed CNN achieved higher Sensitivity or Recall (about 0.88) and Precision values (about 0.93) than the other methods [13]. [PERSON] et al. proposed a multiscale CNN for classification of malignant and benign lung nodules.