# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 2 Materials and Methods
### Study Design
#### 2.2.1 Public Databases
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Database** & **Annotations** & **Link** & **Author** \\ \hline \multirow{8}{*}{Mammograms} & \multirow{3}{*}{DDSM} & 2620 patients including mediolateral & [[http://www.eng.usf.edu/](http://www.eng.usf.edu/)]([http://www.eng.usf.edu/](http://www.eng.usf.edu/)) \\  & & oblique (MLO) and craniocuadal (CC) & cvprg/Mammography/ \\  & & images for classification. & Database.html \\ \cline{2-4}  & \multirow{3}{*}{BCDR} & 736 biopsy prove lesion of 344 patients, & \\  & & including CC and MLO images & [[https://bcdr.eu/](https://bcdr.eu/)]([https://bcdr.eu/](https://bcdr.eu/)) & [PERSON] et al. [68] \\  & & for classification. & \\ \cline{2-4}  & \multirow{3}{*}{INbreast} & 419 cases, including CC and MLO images & \begin{tabular}{c} http://medicalresearch. \\ inescorto.pt/ \\ breastresearch/index.php/ \\ Get\_INbreast\_Database \\ \end{tabular} & [PERSON] et al. [64] \\ \cline{2-4}  & \multirow{3}{*}{Mini-MIAS} & 322 digitized MLO images of 161 patients & \begin{tabular}{c} [[http://peipa.essex.ac.uk/](http://peipa.essex.ac.uk/)]([http://peipa.essex.ac.uk/](http://peipa.essex.ac.uk/)) \\ info/mias.html \\ \end{tabular} & [PERSON] et al. [69] \\  & & for segmentation, detection, & info/mias.html \\ \hline \multirow{8}{*}{Ultrasound} & \multirow{3}{*}{BUSI} & The dataset consists of 600 female patients. \\  & & The 780 images include 133 normal images & \begin{tabular}{c} [[https://scholar.cu.edu.eg/](https://scholar.cu.edu.eg/)]([https://scholar.cu.edu.eg/](https://scholar.cu.edu.eg/)) \\ 7q=afahmy/pages/dataset \\ detection, and segmentation. \\ \end{tabular} & [PERSON] et al. [70] \\  & & & \\ \cline{2-4}  & \multirow{3}{*}{DDBUI} & 285 cases and 1132 images in total & [[https://www.atlantis-press.com/](https://www.atlantis-press.com/)]([https://www.atlantis-press.com/](https://www.atlantis-press.com/)) \\  & & for classification. & proceedings/jcis2008/1735 & [PERSON] et al. [71] \\ \cline{2-4}  & \multirow{3}{*}{Dataset A} & Private dataset with 306 (60 malignant & \\  & & and 246 benign) images, which are & goo.gl/[PERSON] & [PERSON] et al. [48] \\ \cline{2-4}  & \multirow{3}{*}{Dataset B} & Private dataset with 163 (53 malignant & \begin{tabular}{c} [PERSON] et al. [66] \\ and 110 benign) images. \\ \end{tabular} \\ \cline{2-4}  & \multirow{3}{*}{SNUH} & Private dataset with a total of 1225 patients & \begin{tabular}{c} [PERSON] et al. [49] \\ \end{tabular} \\ \cline{2-4}  & & with 1687 tumors. & \begin{tabular}{c} [[https://bluebox.ippt.gov.pl/](https://bluebox.ippt.gov.pl/)]([https://bluebox.ippt.gov.pl/](https://bluebox.ippt.gov.pl/)) \\ which are utilized in image \\ processing algorithms. \\ \end{tabular} & \
\begin{tabular}{c} [PERSON] \\ -npiotrzk \\ \end{tabular} \\ \hline \multirow{3}{*}{ImageNet} & \multirow{3}{*}{ImageNet} & 882 US images (678 benign and & \\  & & 204 malignant lesions), which are utilized & http: \\ \cline{1-1}  & & in object recognition, image classification, &
\begin{tabular}{c} http:\\ //www.image-net.org/ \\ \end{tabular} & [PERSON] et al. [73] \\ \cline{1-1}  & & SNUH: Seoul National University Hospital. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the most commonly used public breast cancer databases in the literature.