## 6 Discussion
### Complexity Analysis of the Proposed CAD
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \begin{tabular}{c} **Input Data/** \\ **Feature Size to** \\ **Classifier** \\ \end{tabular} & \begin{tabular}{c} **Amount of Deep** \\ **Network** \\ **Parameters** \\ \end{tabular} & \begin{tabular}{c} **Amount of** \\ **Layers** \\ \end{tabular} & \
\begin{tabular}{c} **Classification** \\ **Time** \\ **(Seconds)** \\ \end{tabular} &
\begin{tabular}{c} **Classification** \\ **Complexity (O)** \\ \end{tabular} \\ \hline \hline \multicolumn{5}{c}{**CNN Models of the Proposed CAD (End-to-End Deep Learning Classification)**} \\ \hline
**ShuffleNet** & 224 \(\times\) 224 \(\times\) 3 & \(\sim\)1.3 M & \(\sim\)30 & 6525 &
\begin{tabular}{c} \(O(k\cdot n\cdot d^{2})\) \\ \(k\): kernel length \\ \(n\): The overall length of the pattern \\ (the amount of input entries) \\ \(d\): dimensionality of presentation \\ \end{tabular} \\ \hline
**EfficientNetB0** & 224 \(\times\) 224 \(\times\) 3 & 5.3 M & 82 & 15,870 &
\begin{tabular}{c} \(O(k\cdot n\cdot d^{2})\) \\ \(k\): kernel length \\ \(n\): The overall length of the pattern \\ (the amount of input entries) \\ \(d\): dimensionality of presentation \\ \end{tabular} \\ \hline \multicolumn{5}{c}{**Layer Level Features**} \\
**(Scenario I of the Proposed CAD)** & & & & \\ \hline \multicolumn{5}{c}{ShuffleNet = 2} & 0.99779 \\ \cline{2-5}
**Layer 3** & Mobile = 2 & 2.5092 \\ \cline{2-5}
**Features** & EfficientB0 = 2 & 0.9251 \\ \hline \multicolumn{5}{c}{ShuffleNet = 544} & **MGSVM** & 1.8921 \\ \cline{2-5}
**Layer 2** & Mobile = 1280 & \(\begin{array}{c}Parameters=s\cdot p+s+1\\ s=number of support vectors \\ \text{\emph{p}: number of features}\\ \text{\emph{n}: number of input samples}\\ \end{array}\) &
\begin{tabular}{c} \(O(n^{2}\cdot p)\) \\ \(p\): number of features \\ \text{\emph{n}: number of input samples}\\ \end{tabular} \\ \hline \multicolumn{5}{c}{**Layer 1**} \\
**Features after** & Mobile = 980 & \(\begin{array}{c}1.4029\\ 2.3265\end{array}\) \\ \hline \multicolumn{5}{c}{**Layer Level Features after NNMF**} \\ \hline \multicolumn{5}{c}{ShuffleNet = 80} & 0.9910 \\ \cline{2-5}
**Layer 2 after** & Mobile = 100 & \(\begin{array}{c}1.0079\\ \text{\emph{n}: number of support vectors}\\ \text{\emph{p}: number of features}\\ \text{\emph{n}: number of input samples}\\ \end{array}\) &
\begin{tabular}{c} \(1.0079\\ \text{\emph{n}: number of features}\\ \text{\emph{n}: number of input samples}\\ \end{array}\) \\ \hline \multicolumn{5}{c}{**Layer 1 after**} \\
**DWT and NNMF** & Mobile = 40 & 0.8532 \\ \cline{2-5}  & EfficientB0 = 60 & 0.8762 \\ \hline \multicolumn{5}{c}{**Layer Level Fusion**} \\
**(Scenario II of the Proposed CAD)** & & & \\ \hline \multicolumn{5}{c}{**Layer 3 Features**} \\
**of the three** & 6 & 0.96421 \\
**CNNs** & & & \\ \hline \multicolumn{5}{c}{**MGSVM**} \\
**Layer 2 Features** & 230 & \(\begin{array}{c}Parameters=s\cdot p+s+1\\ s=number of support vectors \\ \text{\emph{n}: number of input samples}\\ \end{array}\) & 0.95033 & \(\begin{array}{c}O(n^{2}\cdot p)\\ p\): number of features \\ \text{\emph{n}: number of input samples}\\ \end{array}\) \\ \hline \multicolumn{5}{c}{**Layer 1 Features**} \\
**of the three** & 170 & 0.95719 \\ \hline \multicolumn{5}{c}{**Multi-CNN Multi-Layer Level Fusion**} \\
**(Scenario III of the Proposed CAD)** & & & \\ \hline \multicolumn{5}{c}{**MGSVM**} \\
**Features selected** & 350 & \(\begin{array}{c}Parameters=s\cdot p+s+1\\ s=number of support vectors \\ \text{\emph{n}: number of input samples}\\ \end{array}\) & 1.4045 & \(\begin{array}{c}O(n^{2}\cdot p)\\ p\): number of features \\ \text{\emph{n}: number of input samples}\\ \end{array}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: A complexity analysis and classification time for the suggested CAD.