## 3 Materials and Methods
### Methodology
#### 3.4.1 Convolutional Neural Network
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN Model** & \begin{tabular}{c} **Salient** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **No. of** \\ **Layers** \\ \end{tabular} & **Layers** & **Activation Fun.** & \begin{tabular}{c} **Parameters** \\ **(Millions)** \\ \end{tabular} & \begin{tabular}{c} **Error Rate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Input Image** \\ **Size** \\ \end{tabular} \\ \hline \begin{tabular}{c} AlexNet \\ VGG16 \\ \end{tabular} & \begin{tabular}{c} Deeper \\ Fixed-size \\ kernels \\ \end{tabular} & 16 & 13 Conv+3 FC & ReLU+ Softmax & 62.3 & 15.3 & \(227\times 227\times 3\) \\ \begin{tabular}{c} VGG19 \\ \end{tabular} & \begin{tabular}{c} Fixed-size \\ kernels \\ \end{tabular} & 19 & 16 Conv+3 FC & ReLU+ Softmax & 138.4 & 7.3 & \(224\times 224\times 3\) \\ \begin{tabular}{c} GoogleNet \\ \end{tabular} & \begin{tabular}{c} Wider parallel \\ kernels \\ \end{tabular} & 22 & Conv+9 IncepBlock & ReLU+ Softmax & 6.4 & 6.67 & \(224\times 224\times 3\) \\ \
\begin{tabular}{c} ResNet18 \\ \end{tabular} &
\begin{tabular}{c} Short \\ connection \\ \end{tabular} & 18 & Conv+ResNet & ReLU & 11.5 & 3.57 & \(224\times 224\times 3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameter summary of six pre-trained models [29; 39]. Figure 8: Schematic illustration of convolution and max-pooling operation. filters and a nonlinear activation function, the Rectified Linear Unit (ReLu). It also addresses the issue of overfitting by simply using data augmentation and dropout techniques. The AlexNet CNN has achieved top-1 and top-5 error rates of 37.5% and 17.0%, better than prior state-of-the-art approaches [83]. **VGGNet:** The Visual Geometry Group (VGG) is a standard deep CNN architecture with numerous layers. It was initially proposed by [PERSON] et al. (2014) and took second place in the ILSVRC 2014 contest, where the top-1 and top-5 error rates were 23.7 and 6.8%, respectively. There are two distinct variants of this model, 'VGG 16' and 'VGG 19', with 16 and 19 layers, respectively. All of VGG's hidden layers use ReLU (a significant advancement from AlexNet that reduces the training time). Stacks of smaller convolutional kernels are a key element of this design [84]. **GoogleNet:** It is a 22-layer deep network that was the winner of the ILSVRC 2014 contest. The top-5 error of this model reaches 6.67%, which is close to the human image interpretation level [85]. The design was influenced by LeNet, which was developed with the assistance of a specialized module known as 'Inception' [54]. This module reduces the number of intermediate parameters by employing tiny convolutional filters (\(1\times 1\)), which reduces the model computing time. **ResNet:** It is used as a backbone for many computer vision tasks. It was the winner of the ILSVRC 2015 contest, where it achieved a 3.57% error on the ImageNet test set [86]. This model is available in various versions, such as 'ResNet18', 'ResNet50', and 'ResNet101', with 18, 50, and 101 layers, respectively. To create this network, a method known as skip connections was used. This skip connection links directly to the output, rather than passing through the training for the first few layers. In the case of image recognition, the first layer might work for straight-line detection, the second might work for rough texture identification, and the third might be used to recognize specific objects.