## 2 Related Works
CT scan images of LIDC and IDRI datasets were used. SVM and Random Forest were used as classification algorithms of CNN. Their proposed CNN algorithm with Random Forest classifier achieved 86.84% accuracy rate in the classification of lung nodules without using any segmentation methods [14]. [PERSON] et al. proposed CanNet as a CNN model to classify lung CT scan images of the LIDC dataset. Their proposed CanNet contained two convolutional layers, one max pooling and one fully connected layer. In comparison with traditional Artificial Neural Networks (ANN) and LeNet, their proposed CanNet model achieved the highest accuracy rate in the classification of lung CT scan images. The accuracy rate of each LeNet, ANN and CanNet was 56%, 72.5% and 76% respectively [15]. [PERSON] et al. compared the performance of Deep Neural Network (DNN), CNN and Stacked Auto Encoder (SAE) algorithms in the classification of CT scan images of LIDC-IDRI datasets. The results showed that the CNN algorithm surpassed the other two algorithms in the classification of lung CT scan images. CNN, DNN and SAE achieved 84.15%, 82.37% and 82.59% accuracy rate. CNN and SAE achieved the same sensitivity (83.96%) and DNN achieved 80.66% sensitivity [16]. [PERSON] and [PERSON] used CNN for the classification of an ILD dataset of lung CT scan images. They reported results of CNN for classification of healthy, ground-glass opacity, micro-nodules, reticulation, honeycombing and consolidation classes of ILDs being favorable [17]. [PERSON] et al. proposed the CNN architecture to classify lung CT scan images of Data Science Bowl and Kaggle [18]. For nodule detection, they used U-Net as a detection architecture on a Lung Nodule Analysis (LUNA) dataset as an assistant dataset. Results showed that CNN classification architecture achieved 86.6% accuracy rate. The False Positive (FP) rate and False Negative (FN) rate of CNN were 11.9% and 14.7% respectively [19]. [PERSON] et al. used the modified version of ResNet-18 as classification architecture to classify the Data Science Bowl and Kaggle lung CT scan images [18]. Moreover, Luna dataset was used for nodule detection. Results showed that their modified ResNet-18 achieved better results than the original ResNet-18 in terms of sensitivity, specificity and F1. Their achieved sensitivity, specificity, F1, and log loss values were 0.538, 0.648, 0.33, and 0.52712 respectively [20]. [PERSON] et al. used modified 3D U-Net with Region Proposal Networks (RPN) for nodule detection of Data Science Bowl and Kaggle lung CT scan images. Moreover, to evaluate the probability of detected nodule Leaky Noisy-OR method have been integrated into their used classification method. Their used method achieved 0.3998 of log-loss [21]. [PERSON] et al. proposed a CNN architecture in order to classify the Data Science Bowl and Kaggle lung CT scan images. Results showed that their proposed classification architecture was 0.87, 0.991, 0.95, and 0.20 for sensitivity, specificity, F1, and log-loss, respectively [22]. [PERSON] et al. used a 3D-AlexNet architecture to classify the Data Science Bowl and Kaggle lung CT scan images [18]. They compared their used 3D-AlexNet architecture with different input sizes and epoch sizes. Experimental results showed that their highest achieved accuracy rate was 87.5% [23].