## 3 Preliminaries
### Deep Learning and CNN  
_Convolutional neural networks (CNN or ConvNet)_ are a type of deep learning-based artificial neural network (ANN) that is most commonly applied on the visual image classification in the multiclass data set [33]. The CNN is not a fully connected network, and, therefore, it reduces the computational intensity [34]. This characteristic makes CNN a better choice for image classification problems [35]. A classical model of CNN consists of the following layers. * Convolution Layer: The computer stores image data as a matrix where every individual pixel value of the image is preserved. In this layer, different filters play active roles. A filter is also a matrix, but smaller than the input matrix of any image. In a convolution layer, every filter dimension is the same, but values may differ. When an image is fed into one of these filters, the filter scans the matrix of the image, performs a dot product between the matrix value of the image and filter, adds all the values and a new matrix is generated as an output of this layer. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Studies** & **Year** & \begin{tabular}{c} **Food Calorie** \\ **Estimation** \\ \end{tabular} & **Real Time** & \begin{tabular}{c} **Optimize Time** \\ **Complexity** \\ \end{tabular} & \
\begin{tabular}{c} **Optimize Space** \\ **Complexity** \\ \end{tabular} &
\begin{tabular}{c} **Satisfactory** \\ **Score** \\ \end{tabular} \\ \hline [PERSON] et al. [14] & 2010 & – & – & – & – & ✓ \\ \hline [PERSON] et al. [15] & 2014 & ✓ & – & ✓ & ✓ & – \\ \hline [PERSON] \& Li [16] & 2017 & ✓ & – & – & – & ✓ \\ \hline [PERSON] et al. [17] & 2018 & ✓ & – & ✓ & ✓ & – \\ \hline [PERSON] et al. [18] & 2019 & – & – & – & – & ✓ \\ \hline [PERSON] et al. [31] & 2019 & – & ✓ & ✓ & ✓ & – \\ \hline [PERSON] et al. [13] & 2020 & ✓ & – & – & – & ✓ \\ \hline [PERSON] et al. [19] & 2020 & ✓ & – & – & – & ✓ \\ \hline [PERSON] et al. [20] & 2020 & ✓ & – & – & – & ✓ \\ \hline [PERSON] et al. [25] & 2020 & ✓ & – & – & – & ✓ \\ \hline [PERSON] et al. [22] & 2021 & ✓ & – & – & – & – \\ \hline [PERSON] et al. [23] & 2021 & ✓ & – & – & – & – \\ \hline [PERSON] et al. [21] & 2021 & ✓ & – & – & – & ✓ \\ \hline [PERSON] et al. [24] & 2021 & ✓ & – & – & – & – \\ \hline [PERSON] et al. [26] & 2021 & ✓ & – & – & – & – \\ \hline [PERSON] \& [PERSON] [27] & 2021 & ✓ & – & – & – \\ \hline [PERSON] \& [PERSON] [28] & 2021 & ✓ & ✓ & – & – & ✓ \\ \hline [PERSON] et al. [30] & 2021 & – & – & – & – & ✓ \\ \hline [PERSON] [29] & 2022 & ✓ & – & – & – & ✓ \\ \hline Proposed system & 2022 & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}  
* Here, ‘\(\checkmark\)’ means covered and ‘\(\checkmark\)’ means not covered.  
\end{table} 
Table 1: Comparative analysis of food calorie systems \({}^{a}\). * Max Pooling Layer: The max-pooling layer is commonly used after every convolution layer. The main task of this max-pooling layer is the feature extraction. It finds and extracts the dominant feature from the matrix generated in the convolution layer, ignoring the less important ones. This makes the deep learning model much more efficient. * Dense Layer: The dense layer is a fully connected layer. Every neuron or filter of the dense layer is connected to every output node of the previous layer. It is actually a small traditional neural network inside the CNN [36]. It feeds all outputs from the previous layer to all its neurons where each neuron provides one output to the next layer. * ReLu (Rectified Linear unit) Activation: This activation function improves the decision and nonlinear features of the network without changing the receptive fields of the convolution layer. ReLU is often preferred over other nonlinear functions used in CNNs (such as hyperbolic tangent, absolute of hyperbolic tangent, and sigmoid) because it trains the neural network several times faster without a significant penalty to generalization accuracy.