## 4 Materials and Methods
### ALT-CNN-DENSE Net Architecture  
After the selection phase, the slices are constructed into 3D volumes and input to the proposed ALT-CNN-DENSE _Net_ model. The proposed architecture is illustrated in Figure 5, where it depicts the characterizing blocks of alternating convolution and average pooling layers followed by a drop out layer and multiple dense layers. The structure of the network architecture is detailed in Table 2. ALT-CNN-DENSE _Net_ uses 3D kernels for the convolution process on the input CT volumes, which enables the extraction of both spatial and spectral features [34]. Such capability provides an advantage over 1D and 2D CNNs. However, this comes at the cost of increased complexity. Thus, the sequence of blocks of alternating convolution (CONV) and pooling layers are devised for dimensionality reduction and subsequent complexity moderation. Pooling eliminates redundant and irrelevant information reducing overfitting. Also, it provides spatial translation invariance [35]. In addition to the known advantages of pooling, the downsampling generates bird's eye view feature maps for the following CONV layers. The generated feature maps aid the early detection of 3D primitives reducing the CNN depth. The CONV layers comprise different filter sizes to extract the multiscale inter-slices granule details in each volumetric input. The architecture has inherited the residual connectivity from RESNET. Average pooling layers as well as global average layer was taken from InceptionV3. Residual connections allow gradients to flow through a network directly, without passing through activation functions. At this point, the convolved features are passed between layers with the residual. This has the advantage to compensate any loss in the features between extensive mathematical calculations of layers as well as preserve spatial and temporal features in slices at the same time. Average pooling is used instead of Max pooling to guarantee that pixels with their surroundings relations are taken into consideration and not ignored. Average pooling maintains inter and intra-slices locality spatiotemporal information, unlike in Max Pooling where specific features are selected despite of location. The position of the tumor is critical for lung tumor phenotyping and cancer staging, which explains why average pooling gives higher performance with the problem considered here. The flatten layer is added to convert the extracted features from the previous Convolution-AveragePooling (CNN-AVP) block to a 1D vector. The features vector is fed into a fully connected network of a set of dense layers. A drop out layer is added to reduce overfitting and improve generalization error. The deep dense layers concatenate the feature maps from all of the previous nodes and forward them to the following layers. New comprehensive feature maps are created from the fully connected dense network, which is expected to improve the CNN performance [36]. The softmax layer of this network outputs (n) classifications. To sum up, the developed architecture offers advantages of architecture depth reduction, extraction of both spatial and spectral features, generation of multiscale feature maps, reduced risk of overfitting and features reuse.