# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 4 Discussion and Conclusions
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Reference** & **Application** & **Method** & **Dataset** & **Acc (\%)** & **Sen (\%)** & **Spec (\%)** & **AUC (\%)** & **Error (\%)** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [35] \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} US image \\ segmentation \\ \end{tabular} } & ML decision tree & Private breast US & 77.7 & 74.0 & 82.0 & 80 & - \\ \cline{3-8}  & & \begin{tabular}{c} ANN \\ Random forest \\ \end{tabular} & \begin{tabular}{c} images, where 133 cases \\ are benign and \\ \end{tabular} & 78.1 & 78.0 & 78.2 & 82 & - \\ \cline{3-8}  & & \begin{tabular}{c} SVM \\ region-based \\ \end{tabular} & \begin{tabular}{c} 150 cases are malignant \\ \end{tabular} & 77.7 & 77.3 & 78.2 & 84 & - \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [188] \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} 3D US image \\ segmentation \\ \end{tabular} } & Preprocessing: morphological reconstruction; segmentation: region-based approach & Private database with 21 cases, with masses prior to biopsy & 85.7 & - & - & - & - \\ \cline{3-8}  & & & \begin{tabular}{c} The private dataset \\ consisting of \\ \(227\) elastography \\ images, with 135 benign \\ tumors and \\ \end{tabular} & 93.4 & 88.6 & 97.1 & 94.7 & - \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] \\ et al. [147] \\ \end{tabular} } & US image segmentation & DL-CNN architecture U-net & The private dataset containing 221 BUS images & 82.52 & 78.66 & 18.59 & - & - \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [189] \\ \end{tabular} } & US image classification & ML fuzzy c-means and backpropagation ANN & 178 breast US & 95.86 & 95.14 & 96.58 & 95.85 & - \\ \cline{3-8}  & & \begin{tabular}{c} SD2 breast US \\ (275 benign and \\ 245 malignant lesions) \\ \end{tabular} & 82.4 & 78.7 & 85.7 & 89.6 & - \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} [PERSON], et al. \\ [190] \\ \end{tabular} } & US image classification & Deep polynomial network & 200 pathology-proven breast US images & 92.40 & 92.67 & 91.36 & - & - \\ \cline{3-8}  & & &
\begin{tabular}{c} A total of \\ 200 pathology-proven breast US images \\ \end{tabular} & 92.40 & 92.67 & 91.36 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._techniques for high-resolution [58] data augmentation, segmentation, and classification. The most commonly CNNs used are AlexNet, VGG, ResNet, DenseNet, Inception (GoogleNet), LeNet, and UNet, which employ recent Python libraries for implementing CNNs, such as Tensorflow, Caffe, and Keras, with different hyper-parameters to training the network [55]. Most of these DL architectures use a large data set; thus, it is required to apply an augmentation technique to avoid overfitting and to have better performance during classification. In this sense, the researchers mentioned in Table 6[145; 168; 180; 181] and Table 7[35; 49; 62; 66; 152; 182] the authors used transfer learning and ensemble methods, such as data augmentation, to improve the performance of the CNN network, reaching an 89.86% accuracy and 0.9578% AUC in DM, and an AUC of 0.68% on US images. Furthermore, [PERSON] et al. [165] showed that the results obtained with a GAN for breast tumor segmentation outperformed the UNet model, and the SegNet and ERFNet models yielded the worst segmentation results on US images. In addition, according to [PERSON] et al. [37], DL techniques could potentially change the design paradigm of CADx systems due to their several advantages over the traditional CAD systems. These are as follows: First, DL can directly extract features from the training data. Second, the feature selection process will be significantly simplified. Third, the three steps of feature extraction, selection, and classification can be realized within the same deep architecture. Thus, SDAE architecture can potentially address the issues of high variation in either the shape or appearance of lesions/tumors.