# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 3 Results
### Performance Metrics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & **Database** & **Deep CNN Model** & \begin{tabular}{c} **Acc** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Sen** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Spec** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **F1 Score** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{9}{*}{\begin{tabular}{c} [PERSON] et al. \\ [49] \\ \end{tabular} } & \multirow{9}{*}{BUSI SNUH} & \multirow{9}{*}{WGGNet-like} & \multirow{9}{*}{73.65} & \multirow{9}{*}{93.12} & \multirow{9}{*}{89.34} & \multirow{9}{*}{80.74} & \multirow{9}{*}{91.98} \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Moon et al. \\ [49] \\ \end{tabular} } & \multirow{3}{*}{BUSI SNUH} & \multirow{3}{*}{WGG19} & \multirow{3}{*}{88.7} & \multirow{3}{*}{0.848} & \multirow{3}{*}{0.897} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{93.6} \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [152] \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Private dataset consisting of 579 \\ benign and 464 malignant cases \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Single Shot Detector \\ (SSD)300 + ZFNet \\ \end{tabular} } & \multirow{3}{*}{96.89} & \multirow{3}{*}{67.23} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{79.38} & \multirow{3}{*}{-} \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [62] \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Private database with a total of \\ 7408 US images with \\ 3154 malignant lesions \\ \end{tabular} } & \multirow{3}{*}{CNN-based GoogleNet} & \multirow{3}{*}{91.23} & \multirow{3}{*}{84.29} & \multirow{3}{*}{96.07} & \multirow{3}{*}{-} & \multirow{3}{*}{91} \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} [PERSON] et al. \\ [35] \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} Private database containing \\ 283 breast US images (133 cases \\ are benign and 150 cases \\ are malignant) \\ \end{tabular} } & \multirow{3}{*}{ANN} & \multirow{3}{*}{78.1} & \multirow{3}{*}{78} & \multirow{3}{*}{78.2} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & \multirow{3}{*}{82.3} \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The quantitative indicators that were used to evaluate different CNN architectures’ performances on US datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & **Application** & **Method** & **Dataset** & **Acc** & **Sen** & **Spec** & **AUC** & **Error** \\  & & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{3}{*}{ \begin{tabular}{c} [PERSON] \\ [183] \\ \end{tabular}  } & \multirow{3}{*}{DM classification} & \multirow{3}{*}{ML wavelet neural network} & \multirow{3}{*}{93.67} & \multirow{3}{*}{94.16} & \multirow{3}{*}{92.10} & \multirow{3}{*}{96.85} & \multirow{3}{*}{96.85} \\  & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \cline{3-3} \cline{5-5}  & & & & & & & & \\ \hline \multirow{3}{*}{ \begin{tabular}{c} [PERSON] \\ et al. [184] \\ \end{tabular}  } & \multirow{3}{*}{DM classification} & \multirow{3}{*}{ \begin{tabular}{c} ML with transfer learning and \\ features based on ImageNet and CNN architecture \\ \end{tabular}