## 5 Experimental Results and Discussion  
In this section, the output results of two proposed 3D-CNN architectures and both 3D-AlexNet and 3D-GoogleNet architectures on lung CT scan images are presented. Furthermore, the achieved results of proposed architectures are compared with the 3D-AlexNet and 3D-GoogleNet as well. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Layer Type** & **Number of Kernels** & **Kernel Size** & **Output Size** \\ \hline Convolutional & 96 & \(11\times 11\times 11\) & \(96\times 55\times 55\times 55\) \\ Max pooling & & \(3\times 3\times 3\) & \(96\times 27\times 27\times 27\) \\ Convolutional & 256 & \(5\times 5\times 5\) & \(256\times 27\times 27\times 27\) \\ Max pooling & & \(3\times 3\times 3\) & \(256\times 13\times 13\times 13\) \\ Convolutional & 384 & \(3\times 3\times 3\) & \(384\times 13\times 13\times 13\) \\ Convolutional & 256 & \(3\times 3\times 3\) & \(256\times 13\times 13\times 13\) \\ Max pooling & & \(3\times 3\times 3\) & \(256\times 6\times 6\times 6\) \\ Fully connected & & & \(4096\times 1\times 1\times 1\) \\ Fully connected & & & \(4096\times 1\times 1\times 1\) \\ Fully connected with softmax & & & \(2\times 1\times 1\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of AlexNet architecture in the classification of lung CT scan images. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Layer Type** & **Number of Kernels** & **Kernel Size** & **Output Size** \\ \hline Convolutional & 64 & \(7\times 7\times 7\) & \(64\times 112\times 112\times 112\) \\ Max pooling & & \(3\times 3\times 3\) & \(64\times 56\times 56\times 56\) \\ Convolutional & 192 & \(3\times 3\times 3\) & \(192\times 56\times 56\times 56\) \\ Max pooling & & \(3\times 3\times 3\) & \(192\times 28\times 28\times 28\) \\ Inception 3(a) & & & \(256\times 28\times 28\times 28\) \\ Inception 3(b) & & & \(480\times 28\times 28\times 28\) \\ Max pooling & & \(3\times 3\times 3\) & \(480\times 14\times 14\times 14\) \\ Inception 4(a) & & & \(512\times 14\times 14\times 14\) \\ Inception 4(b) & & & \(512\times 14\times 14\times 14\) \\ Inception 4(c) & & & \(512\times 14\times 14\times 14\) \\ Inception 4(d) & & & \(528\times 14\times 14\times 14\) \\ Inception 4(e) & & & \(832\times 14\times 14\times 14\) \\ Max pooling & & \(3\times 3\times 3\) & \(832\times 7\times 7\times 7\) \\ Inception 5(a) & & & \(832\times 7\times 7\times 7\) \\ Inception 5(b) & & & \(1024\times 7\times 7\times 7\) \\ Avg pooling & & \(7\times 7\times 7\) & \(1024\times 1\times 1\times 1\) \\ Fully connected & & & \(1024\times 1\times 1\times 1\) \\ Fully connected with softmax & & & \(2\times 1\times 1\times 1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of GoogleNet architecture in the classification of lung CT scan images. To evaluate the performance of 3D-CNN proposed architectures (straight 3D-CNN and hybrid 3D-CNN) and two 3D-AlexNet and 3D-GoogleNet architectures, 75% of the dataset is considered as a train set and 25% is considered as a test set. A summary of the accuracy rate, sensitivity, precision, and specificity of all four architectures is presented in Table 4. Moreover, the accuracy of all four architectures with epochs is depicted in Figure 5.  
By considering the achieved results (Table 4 and Figure 5), it can be demonstrated that both proposed 3D-CNN architectures (straight 3D-CNN and hybrid 3D-CNN) have achieved a higher accuracy rate in the classification of lung CT scan images compared to the 3D-Alexnet and 3D-GoogleNet. As a result, the proposed hybrid 3D-CNN using RBF-based SVM classifier surpassed all other architectures by 91.81% accuracy rate and the second best accuracy rate belongs to the proposed straight 3D-CNN (90.23%) which used softmax classifier in the classification of lung CT scan images. However, in comparison with 3D-Alexnet having 85.79% accuracy rate, 3D-GoogleNet achieved a higher accuracy rate (87.95%).